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Model: tianchuang/Qwen-3-8B-RHEA-property-predictor Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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metrics:
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- accuracy
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- recall
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- precision
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- r_squared
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- mse
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- mae
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base_model:
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- Qwen/Qwen3-8B
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tags:
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- text-generation-inference
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- materials-science
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- qwen3
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- classification
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- regression
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---
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# Model Overview
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This repository contains the weights for the **Qwen-3-8B-RHEA-property-predictor**, fine-tuned for refractory high entropy alloys property prediction and phase classification tasks.
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## Training Details
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### Prompt Template
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Training prompts follow the template:
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**property prediction task**
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> “You are a materials science expert. Predict the {**property name**} for the following refractory high entropy alloy.”
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property name includs density, hardness, compressive yield strength at room temperature, compressive strain at room temperature,
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compressive yield strength at 1073K, and compressive yield strength at 1273K
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**phase classification task**
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> “You are a materials science expert. Determine whether the given refractory high entropy alloy has {**single solution phase/intermetallic phase**}.”
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Each query was formatted as:
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> “{**composition**} alloy prepared by {**process description text**}”
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### Hyperparameters & Settings
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* **Task:** Binary classification / Regression
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* **GPU:** 4 × NVIDIA GeForce RTX 3090
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* **Seed:** 42
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* **Final Epoch:** 4
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* **per-device batch size:** 2
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* **gradient accumulation:** 8
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* **Training Objective:** Full fine-tuning with CrossEntropyLoss
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* **Sequence Length:** 1024 tokens
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* **Dataset:**
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* Train: `train_data-all.jsonl`
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* Validation: `val_data-all.jsonl`
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* **Dataset Source:** The datasets are available at (https://huggingface.co/datasets/tianchuang/RHEA-mechanical-property)
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---
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## Validation Metrics
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Metrics are from model-calling evaluation.
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**Property prediction (val)**
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| Metric | YS task | Strain task | YS-1073 task | YS-1273 task | hardness task | density task |
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| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
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| R2 | 0.533 | 0.185 | 0.565 | 0.671 | 0.561 | 0.886 |
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| MAE | 237.4 | 7.73 | 192.1 | 117.1 | 76.3 | 0.46 |
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| RMSE | 324.8 | 9.93 | 251.4 | 149.1 | 109.6 | 0.69 |
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**Phase classification (val)**
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| Metric | SS task | IM task |
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| :--- | :--- | :--- |
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| Precision | 0.833 | 0.849 |
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| Recall | 0.847 | 0.865 |
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| F1 score | 0.840 | 0.857 |
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| Accuracy | 0.858 | 0.886 |
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---
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## How to Use
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You can load and run inference with this model using the `transformers` library. The model uses the ChatML prompt format.
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
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# 1. Load model and tokenizer
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model_id = "tianchuang/Qwen-3-8B-RHEA-property-predictor"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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# 2. Prepare your input data
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instruction = "Is the material BaTiO3 likely synthesizable? Answer with P (positive) or N (negative)."
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input_text = "" # Leave empty if no additional context is needed
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# 3. Format the input using the ChatML template
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if input_text:
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prompt = f"<|im_start|>system\nYou are a materials science expert.<|im_end|>\n<|im_start|>user\n{instruction}\n{input_text}<|im_end|>\n<|im_start|>assistant\n"
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else:
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prompt = f"<|im_start|>system\nYou are a materials science expert.<|im_end|>\n<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
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# 4. Tokenize and generate response
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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generation_config = GenerationConfig(
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max_new_tokens=64,
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do_sample=True,
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temperature=0.6,
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top_p=0.9,
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top_k=50,
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pad_token_id=tokenizer.eos_token_id,
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eos_token_id=tokenizer.eos_token_id,
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)
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outputs = model.generate(**inputs, generation_config=generation_config)
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# 5. Decode and parse the prediction
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full_response = tokenizer.decode(outputs[0], skip_special_tokens=False)
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assistant_response = full_response.split("<|im_start|>assistant")[-1]
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clean_response = assistant_response.replace("<|im_end|>", "").strip()
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print(f"Prediction: {clean_response}")
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added_tokens.json
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{
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"</think>": 151668,
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}
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chat_template.jinja
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chat_template.jinja
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{%- 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 message.content is string and not(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.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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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' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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||||
{%- if message.reasoning_content is string %}
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||||
{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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||||
{%- if '</think>' in content %}
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||||
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.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 }}
|
||||
{%- endif %}
|
||||
{%- if message.tool_calls %}
|
||||
{%- 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 %}
|
||||
{%- 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' }}
|
||||
{{- 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 %}
|
||||
68
config.json
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config.json
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{
|
||||
"architectures": [
|
||||
"Qwen3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151645,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 4096,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 12288,
|
||||
"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",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 40960,
|
||||
"max_window_layers": 36,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 8,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.53.2",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
13
generation_config.json
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generation_config.json
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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": "4.53.2"
|
||||
}
|
||||
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merges.txt
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151388
merges.txt
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Load Diff
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407
model.safetensors.index.json
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407
model.safetensors.index.json
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|
||||
{
|
||||
"metadata": {
|
||||
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|
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|
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3
training_args.bin
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@@ -0,0 +1,3 @@
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151645
vocab.json
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151645
vocab.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user