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Model: mims-harvard/bio-posttrain-qwen3-1.7b-rna-sft
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---
license: apache-2.0
base_model: Qwen/Qwen3-1.7B
tags:
- biology
- bio-posttrain
- rna-sft
- rna
library_name: transformers
---
# Bio-posttrain Qwen3-1.7B RNA SFT
RNA supervised fine-tuning (SFT) checkpoint from [How Post-Training Shapes Biological Reasoning Models](https://huggingface.co/collections/mims-harvard/bio-posttrain).
## Model details
- **Base model:** `Qwen/Qwen3-1.7B`
- **RNA embeddings:** Precomputed TranscriptFormer (2048-d), projected via `rna_projection.pt`
- **LoRA:** rank 32, alpha 64
- **Validation loss:** 0.5394
## Files
| File | Description |
|------|-------------|
| `model.safetensors` | Merged LLM weights |
| `rna_projection.pt` | Linear map RNA embed (2048) → text hidden |
| `rna_model_config.json` | Architecture metadata |
## Loading
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained("mims-harvard/bio-posttrain-qwen3-1.7b-rna-sft", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("mims-harvard/bio-posttrain-qwen3-1.7b-rna-sft")
proj = torch.load("rna_projection.pt", map_location="cpu")
```
RNA sequence embeddings are supplied offline at inference time. See the `rna_models` code in the BioReason repository.
## Collection
Part of the [Bio-posttrain](https://huggingface.co/collections/mims-harvard/bio-posttrain) collection.

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{%- set rna_count = namespace(value=0) %}{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# 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>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\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" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- 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>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' }} {%- if message.content is string %}{{- message.content + '<|im_end|>' + '\n' }}{%- else %}{%- for content in message.content %}{%- if content.type == 'rna' or 'rna' in content %}{%- set rna_count.value = rna_count.value + 1 %}{%- if add_rna_id %}RNA Sequence {{- rna_count.value }}: {%- endif %}<|rna_start|><|rna_pad|><|rna_end|>{%- elif 'text' in content %}{{- content.text }}{%- endif %}{%- endfor %}{{- '<|im_end|>' + '\n' }}{%- endif %}{%- elif message.role == "assistant" %}
{%- set content = message.content[0].text %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in message.content %}
{%- set content = message.content[0].text.split('</think>')[-1].lstrip('\n') %}
{%- set reasoning_content = message.content[0].text.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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{
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 151643,
"dtype": "float32",
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 6144,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
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"full_attention"
],
"max_position_embeddings": 40960,
"max_window_layers": 28,
"model_type": "qwen3",
"num_attention_heads": 16,
"num_hidden_layers": 28,
"num_key_value_heads": 8,
"pad_token_id": null,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 1000000,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": false,
"transformers_version": "5.1.0",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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{
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"pad_token_id": 151643,
"temperature": 0.6,
"top_k": 20,
"top_p": 0.95,
"transformers_version": "5.1.0"
}

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Missing keys:
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{
"model_type": "rna_llm",
"text_model_name": "Qwen/Qwen3-1.7B",
"chat_template_family": "qwen",
"rna_embed_dim": 2048,
"max_rna_seq_len": 1,
"max_length_text": 4096,
"lora_rank": 32,
"lora_alpha": 64,
"lora_dropout": 0.05,
"source_checkpoint": "/n/holylfs06/LABS/mzitnik_lab/Lab/lfesser/evo_tfm/hf_upload_models/Qwen3-1.7B-RNA-SFT-paper-e5-val_loss-0.5394.ckpt",
"epoch": 2,
"global_step": 515,
"hyper_parameters": {
"text_model_name": "Qwen/Qwen3-1.7B",
"rna_embed_dim": 2048,
"lora_rank": 32,
"lora_alpha": 64,
"lora_dropout": 0.05,
"max_length_text": 4096,
"chat_template_family": "qwen"
},
"special_tokens": [
"<|rna_start|>",
"<|rna_pad|>",
"<|rna_end|>"
],
"notes": "RNA embeddings are precomputed offline (TranscriptFormer, 2048-d) and projected via rna_projection.pt. Use the evo_tfm/rna_models inference code to load."
}

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{
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": [
"<|rna_start|>",
"<|rna_pad|>",
"<|rna_end|>"
],
"is_local": false,
"model_max_length": 131072,
"pad_token": "<|im_end|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}