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Model: mims-harvard/bio-posttrain-qwen3-1.7b-dna-rl Source: Original Platform
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33
README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-1.7B
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tags:
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- biology
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- bio-posttrain
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- dna-rl
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- dna
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library_name: transformers
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---
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# Bio-posttrain Qwen3-1.7B DNA RL
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DNA reinforcement learning (GRPO) checkpoint from [How Post-Training Shapes Biological Reasoning Models](https://huggingface.co/collections/mims-harvard/bio-posttrain).
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## Model details
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- **Base model:** `Qwen/Qwen3-1.7B`
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- **DNA encoder:** Evo2 `evo2_1b_base` (frozen; not included)
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- **Embedding layer:** `blocks.20.mlp.l3`
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- **SFT pool:** sft256 / LoRA rank 16
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- **GRPO LoRA:** rank 16, alpha 32
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- **Checkpoint:** step 1156
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This repo contains the merged text LLM plus `dna_projection.pt`.
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## Loading
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Same layout as the DNA SFT model — see [bio-posttrain-qwen3-1.7b-dna-sft](https://huggingface.co/mims-harvard/bio-posttrain-qwen3-1.7b-dna-sft).
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## Collection
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Part of the [Bio-posttrain](https://huggingface.co/collections/mims-harvard/bio-posttrain) collection.
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84
chat_template.jinja
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chat_template.jinja
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{%- set dna_count = namespace(value=0) %}{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and not(message.content is string and message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' }} {%- if message.content is string %}{{- message.content + '<|im_end|>' + '\n' }}{%- else %}{%- for content in message.content %}{%- if content.type == 'dna' or 'dna' in content %}{%- set dna_count.value = dna_count.value + 1 %}{%- if add_dna_id %}DNA Sequence {{- dna_count.value }}: {%- endif %}<|dna_start|><|dna_pad|><|dna_end|>{%- elif 'text' in content %}{{- content.text }}{%- endif %}{%- endfor %}{{- '<|im_end|>' + '\n' }}{%- endif %}{%- elif message.role == "assistant" %}
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{%- set content = message.content[0].text %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in message.content %}
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{%- set content = message.content[0].text.split('</think>')[-1].lstrip('\n') %}
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{%- set reasoning_content = message.content[0].text.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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config.json
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config.json
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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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"dtype": "float32",
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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": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 6144,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 40960,
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"max_window_layers": 28,
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"model_type": "qwen3",
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"num_attention_heads": 16,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": false,
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"transformers_version": "5.1.0",
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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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21
dna_model_config.json
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dna_model_config.json
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{
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"model_type": "dna_llm",
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"dna_model_name": "evo2_1b_base",
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"dna_is_evo2": true,
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"dna_embedding_layer": "blocks.20.mlp.l3",
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"dna_hidden_size": 1920,
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"text_hidden_size": 2048,
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"max_length_dna": 2048,
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"max_length_text": 2048,
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"special_tokens": [
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"<|dna_start|>",
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"<|dna_pad|>",
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"<|dna_end|>"
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],
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"notes": "DNA encoder weights are not bundled. Load Evo2 evo2_1b_base separately and apply dna_projection.pt to map encoder embeddings into the text model.",
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"stage": "grpo",
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"sft_pool": "sft256-r16",
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"lora_rank": 16,
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"lora_alpha": 32,
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"checkpoint_step": 1156
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}
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3
dna_projection.pt
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dna_projection.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:f140154bfda77619029aabd4c1a4891020269c0075dfb176c01c205d4a0596ea
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size 15738781
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generation_config.json
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"temperature": 0.6,
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"top_k": 20,
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"top_p": 0.95,
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"transformers_version": "5.1.0"
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}
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missing_and_unexpected_keys.txt
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missing_and_unexpected_keys.txt
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Missing keys:
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Unexpected keys:
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3
model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:8c4ee220afc8650e779f6461059a4b133d330706cb190eb2ea22ded8350369d4
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size 8126995136
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tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:9c4abfdbaefdacf23a53129abdc73b56451806c0de7f5ebffd7463bf2c96db11
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size 11423216
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tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [
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"<|dna_start|>",
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"<|dna_pad|>",
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"<|dna_end|>"
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],
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"is_local": false,
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"model_max_length": 131072,
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"pad_token": "<|im_end|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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