Add lmul-attention version of deepseek-ai/DeepSeek-R1-0528-Qwen3-8B
Browse files- .gitattributes +1 -0
- chat_template.jinja +14 -0
- config.json +35 -0
- lmul.py +88 -0
- special_tokens_map.json +23 -0
- tokenizer.json +3 -0
- tokenizer_config.json +242 -0
.gitattributes
CHANGED
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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+
tokenizer.json filter=lfs diff=lfs merge=lfs -text
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chat_template.jinja
ADDED
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@@ -0,0 +1,14 @@
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{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='', is_first_sp=true, is_last_user=false) %}{%- for message in messages %}{%- if message['role'] == 'system' %}{%- if ns.is_first_sp %}{% set ns.system_prompt = ns.system_prompt + message['content'] %}{% set ns.is_first_sp = false %}{%- else %}{% set ns.system_prompt = ns.system_prompt + '
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' + message['content'] %}{%- endif %}{%- endif %}{%- endfor %}{{ bos_token }}{{ ns.system_prompt }}{%- for message in messages %}{% set content = message['content'] %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{%- set ns.is_first = false -%}{%- set ns.is_last_user = true -%}{{'<|User|>' + content + '<|Assistant|>'}}{%- endif %}{%- if message['role'] == 'assistant' %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{% endif %}{%- if message['role'] == 'assistant' and message['tool_calls'] is defined and message['tool_calls'] is not none %}{%- set ns.is_last_user = false -%}{%- if ns.is_tool %}{{'<|tool▁outputs▁end|>'}}{%- endif %}{%- set ns.is_first = false %}{%- set ns.is_tool = false -%}{%- set ns.is_output_first = true %}{%- for tool in message['tool_calls'] %}{%- if not ns.is_first %}{%- if content is none %}{{'<|tool▁calls▁begin|><|tool▁call▁begin|>' + tool['type'] + '<|tool▁sep|>' + tool['function']['name'] + '
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' + '```json' + '
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' + tool['function']['arguments'] + '
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' + '```' + '<|tool▁call▁end|>'}}{%- else %}{{content + '<|tool▁calls▁begin|><|tool▁call▁begin|>' + tool['type'] + '<|tool▁sep|>' + tool['function']['name'] + '
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' + '```json' + '
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' + tool['function']['arguments'] + '
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' + '```' + '<|tool▁call▁end|>'}}{%- endif %}{%- set ns.is_first = true -%}{%- else %}{{'
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' + '<|tool▁call▁begin|>' + tool['type'] + '<|tool▁sep|>' + tool['function']['name'] + '
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' + '```json' + '
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' + tool['function']['arguments'] + '
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' + '```' + '<|tool▁call▁end|>'}}{%- endif %}{%- endfor %}{{'<|tool▁calls▁end|><|end▁of▁sentence|>'}}{%- endif %}{%- if message['role'] == 'assistant' and (message['tool_calls'] is not defined or message['tool_calls'] is none)%}{%- set ns.is_last_user = false -%}{%- if ns.is_tool %}{{'<|tool▁outputs▁end|>' + content + '<|end▁of▁sentence|>'}}{%- set ns.is_tool = false -%}{%- else %}{{content + '<|end▁of▁sentence|>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_last_user = false -%}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<|tool▁outputs▁begin|><|tool▁output▁begin|>' + content + '<|tool▁output▁end|>'}}{%- set ns.is_output_first = false %}{%- else %}{{'
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<|tool▁output▁begin|>' + content + '<|tool▁output▁end|>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<|tool▁outputs▁end|>'}}{% endif %}{% if add_generation_prompt and not ns.is_last_user and not ns.is_tool %}{{'<|Assistant|>'}}{% endif %}
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config.json
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@@ -0,0 +1,35 @@
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 12288,
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"max_position_embeddings": 131072,
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"max_window_layers": 36,
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"model_type": "qwen3",
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-06,
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"rope_scaling": {
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"attn_factor": 0.8782488562869419,
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"factor": 4.0,
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"original_max_position_embeddings": 32768,
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"rope_type": "yarn"
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},
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"rope_theta": 1000000,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.52.4",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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lmul.py
ADDED
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@@ -0,0 +1,88 @@
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"""
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PyTorch-native implementation of the L-Mul algorithm.
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"""
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from __future__ import annotations
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import torch
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__all__ = [
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"l_mul_tensor",
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"l_mul_attention",
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]
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def l_mul_tensor(
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x: torch.Tensor, y: torch.Tensor, *, offset: float = 1.0 / 16.0
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) -> torch.Tensor:
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"""
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Approximates `x * y` element-wise using the L-Mul algorithm.
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"""
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sign = torch.sign(x) * torch.sign(y)
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x_abs = torch.abs(x)
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y_abs = torch.abs(y)
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# Decompose tensors into mantissa and exponent.
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# torch.frexp gives mantissa in [0.5, 1.0)
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mx, ex = torch.frexp(x_abs)
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my, ey = torch.frexp(y_abs)
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# The paper's logic implies a mantissa in [1.0, 2.0).
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# We reconstruct this by multiplying the mantissa by 2 and adjusting the exponent.
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mant_a = mx * 2.0
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mant_b = my * 2.0
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exp_a = ex - 1
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exp_b = ey - 1
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# Approximate multiplication using the L-Mul formula
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result_mant = (mant_a - 1.0) + (mant_b - 1.0) + 1.0 + offset
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result_exp = exp_a + exp_b
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# Reconstruct the final number
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result = torch.ldexp(result_mant, result_exp)
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# Apply the correct sign and handle zero inputs
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final_result = sign * result
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final_result[x == 0] = 0
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final_result[y == 0] = 0
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return final_result
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def l_mul_attention(
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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mask: torch.Tensor | None = None,
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dropout: torch.nn.Module | None = None
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) -> tuple[torch.Tensor, torch.Tensor]:
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"""
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Scaled dot-product attention where matrix multiplications are replaced
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by the L-Mul approximation for performance.
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"""
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d_k = query.size(-1)
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+
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# Approximate Q @ K.T using l_mul_tensor.
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# This requires broadcasting and summing to perform the matrix multiplication.
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scores = l_mul_tensor(
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query.unsqueeze(-1),
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key.transpose(-2, -1).unsqueeze(-3)
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).sum(-2)
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scores = scores / (d_k ** 0.5)
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if mask is not None:
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scores = scores.masked_fill(mask == 0, -1e9)
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attn_probs = torch.nn.functional.softmax(scores, dim=-1)
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if dropout is not None:
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attn_probs = dropout(attn_probs)
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# Approximate Attn @ V using l_mul_tensor
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output = l_mul_tensor(
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attn_probs.unsqueeze(-1),
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value.unsqueeze(-3)
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).sum(-2)
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return output, attn_probs
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special_tokens_map.json
ADDED
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@@ -0,0 +1,23 @@
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{
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"bos_token": {
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"content": "<|begin▁of▁sentence|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "<|end▁of▁sentence|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "<|end▁of▁sentence|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:93d5fd6d2f8cf1172ac86cf982e2b88fa6732366b44dc1a32349379a54a6a044
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size 11423346
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tokenizer_config.json
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| 1 |
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{
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| 2 |
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"add_bos_token": false,
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| 3 |
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| 4 |
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| 5 |
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"added_tokens_decoder": {
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| 6 |
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| 7 |
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| 8 |
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| 9 |
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| 10 |
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| 11 |
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| 12 |
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| 13 |
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| 14 |
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| 15 |
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| 16 |
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| 17 |
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| 18 |
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| 19 |
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| 20 |
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| 21 |
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| 22 |
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| 23 |
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| 24 |
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| 25 |
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| 26 |
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| 27 |
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| 28 |
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|
| 29 |
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| 30 |
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| 31 |
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|
| 32 |
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| 33 |
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| 34 |
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| 35 |
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| 36 |
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| 37 |
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| 38 |
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| 39 |
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| 40 |
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| 41 |
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| 42 |
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| 43 |
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| 44 |
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| 45 |
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| 46 |
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| 47 |
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| 48 |
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| 49 |
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| 50 |
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| 51 |
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| 52 |
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| 53 |
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| 54 |
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| 55 |
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| 56 |
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| 57 |
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| 58 |
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| 59 |
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| 60 |
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| 61 |
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| 62 |
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| 63 |
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| 64 |
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| 65 |
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| 66 |
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| 67 |
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| 68 |
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| 69 |
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| 70 |
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| 71 |
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| 72 |
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| 73 |
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| 74 |
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| 75 |
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| 76 |
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| 77 |
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| 78 |
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| 79 |
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| 80 |
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| 81 |
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| 82 |
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| 83 |
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| 84 |
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| 85 |
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| 86 |
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| 87 |
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| 88 |
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| 89 |
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| 90 |
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| 91 |
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| 92 |
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| 93 |
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| 94 |
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| 95 |
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| 96 |
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| 97 |
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| 98 |
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| 99 |
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| 100 |
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| 101 |
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| 102 |
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| 103 |
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| 104 |
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| 105 |
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| 106 |
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| 107 |
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| 108 |
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| 109 |
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| 110 |
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| 111 |
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| 112 |
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| 113 |
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| 114 |
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| 115 |
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| 116 |
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| 117 |
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| 118 |
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| 119 |
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| 120 |
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| 121 |
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| 122 |
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| 123 |
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| 124 |
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| 125 |
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| 126 |
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| 127 |
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| 128 |
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| 129 |
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| 130 |
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| 131 |
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| 132 |
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| 133 |
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| 134 |
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| 135 |
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| 136 |
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| 137 |
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| 138 |
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| 139 |
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| 140 |
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| 141 |
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| 142 |
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| 143 |
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| 144 |
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| 145 |
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| 146 |
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| 147 |
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| 148 |
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| 149 |
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| 150 |
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| 151 |
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| 152 |
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| 153 |
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| 154 |
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| 155 |
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| 156 |
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| 157 |
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| 158 |
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| 159 |
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"content": "<|fim_pad|>",
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| 160 |
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| 161 |
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| 162 |
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| 163 |
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| 164 |
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| 165 |
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| 166 |
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| 167 |
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"content": "<|repo_name|>",
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| 168 |
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| 169 |
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| 170 |
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| 171 |
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| 172 |
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| 173 |
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| 174 |
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| 175 |
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"content": "<|file_sep|>",
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| 176 |
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| 177 |
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| 178 |
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| 179 |
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| 180 |
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| 181 |
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| 182 |
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|
| 183 |
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"content": "<tool_response>",
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| 184 |
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| 185 |
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| 186 |
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| 187 |
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| 188 |
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|
| 189 |
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|
| 190 |
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|
| 191 |
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"content": "</tool_response>",
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| 192 |
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| 193 |
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| 194 |
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| 195 |
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|
| 196 |
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|
| 197 |
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|
| 198 |
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|
| 199 |
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| 200 |
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| 201 |
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| 202 |
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|
| 203 |
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| 204 |
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| 205 |
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|
| 206 |
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|
| 207 |
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| 208 |
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|
| 209 |
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| 210 |
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|
| 211 |
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|
| 212 |
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|
| 213 |
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|
| 214 |
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|
| 215 |
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"content": "<|User|>",
|
| 216 |
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|
| 217 |
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|
| 218 |
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|
| 219 |
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|
| 220 |
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|
| 221 |
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|
| 222 |
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|
| 223 |
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"content": "<|Assistant|>",
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| 224 |
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|
| 225 |
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|
| 226 |
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| 227 |
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|
| 228 |
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|
| 229 |
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}
|
| 230 |
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},
|
| 231 |
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"bos_token": "<|begin▁of▁sentence|>",
|
| 232 |
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|
| 233 |
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"eos_token": "<|end▁of▁sentence|>",
|
| 234 |
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|
| 235 |
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|
| 236 |
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|
| 237 |
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| 238 |
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|
| 239 |
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|
| 240 |
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|
| 241 |
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"use_default_system_prompt": false
|
| 242 |
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}
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