Text Generation
PEFT
Safetensors
Transformers
qwen2
grpo
lora
trl
conversational
text-generation-inference
Instructions to use Gege24/environment_test_affine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Gege24/environment_test_affine with PEFT:
Base model is not found.
- Transformers
How to use Gege24/environment_test_affine with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Gege24/environment_test_affine") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Gege24/environment_test_affine") model = AutoModelForCausalLM.from_pretrained("Gege24/environment_test_affine") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Gege24/environment_test_affine with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Gege24/environment_test_affine" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Gege24/environment_test_affine", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Gege24/environment_test_affine
- SGLang
How to use Gege24/environment_test_affine with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Gege24/environment_test_affine" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Gege24/environment_test_affine", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Gege24/environment_test_affine" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Gege24/environment_test_affine", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Gege24/environment_test_affine with Docker Model Runner:
docker model run hf.co/Gege24/environment_test_affine
Upload task output 1
Browse files- README.md +209 -0
- adapter_config.json +9 -9
- adapter_model.safetensors +3 -0
- chat_template.jinja +54 -5
- loss.txt +1 -0
- special_tokens_map.json +0 -7
- tokenizer_config.json +1 -1
- trainer_state.json +354 -0
- training_args.bin +3 -0
README.md
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---
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base_model: None
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library_name: peft
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pipeline_tag: text-generation
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tags:
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- base_model:adapter:/cache/models/Qwen--Qwen2.5-3B-Instruct
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- grpo
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- lora
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- transformers
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- trl
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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### Framework versions
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- PEFT 0.18.1
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adapter_config.json
CHANGED
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"ensure_weight_tying": false,
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| 10 |
"eva_config": null,
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| 11 |
"exclude_modules": null,
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-
"fan_in_fan_out":
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| 13 |
-
"inference_mode":
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| 14 |
"init_lora_weights": true,
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"layer_replication": null,
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| 16 |
"layers_pattern": null,
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| 17 |
"layers_to_transform": null,
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| 18 |
"loftq_config": {},
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| 19 |
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"lora_alpha":
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| 20 |
"lora_bias": false,
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| 21 |
-
"lora_dropout": 0.
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| 22 |
"megatron_config": null,
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| 23 |
"megatron_core": "megatron.core",
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"modules_to_save": null,
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| 25 |
"peft_type": "LORA",
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| 26 |
"peft_version": "0.18.1",
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| 27 |
"qalora_group_size": 16,
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| 28 |
-
"r":
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| 29 |
"rank_pattern": {},
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| 30 |
"revision": null,
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| 31 |
"target_modules": [
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| 32 |
-
"up_proj",
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| 33 |
"v_proj",
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| 34 |
-
"
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"gate_proj",
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"k_proj",
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"o_proj",
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| 38 |
"down_proj"
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],
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| 40 |
-
"target_parameters":
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| 41 |
"task_type": "CAUSAL_LM",
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| 42 |
"trainable_token_indices": null,
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| 43 |
"use_dora": false,
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| 9 |
"ensure_weight_tying": false,
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"eva_config": null,
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| 11 |
"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": true,
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| 14 |
"init_lora_weights": true,
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| 15 |
"layer_replication": null,
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| 16 |
"layers_pattern": null,
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| 17 |
"layers_to_transform": null,
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"loftq_config": {},
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| 19 |
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"lora_alpha": 256,
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"lora_bias": false,
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"lora_dropout": 0.05,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"peft_version": "0.18.1",
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"qalora_group_size": 16,
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"r": 128,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"v_proj",
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"up_proj",
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"k_proj",
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"gate_proj",
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"o_proj",
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"q_proj",
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"down_proj"
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],
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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| 42 |
"trainable_token_indices": null,
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| 43 |
"use_dora": false,
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adapter_model.safetensors
ADDED
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| 1 |
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version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8770f625ea3546b7a1ccec5ee214f397ae1e299ec3812d8981bb4a76c17c241d
|
| 3 |
+
size 957942768
|
chat_template.jinja
CHANGED
|
@@ -1,5 +1,54 @@
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| 1 |
-
{% if
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| 2 |
-
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| 3 |
-
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| 4 |
-
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| 5 |
-
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| 1 |
+
{%- if tools %}
|
| 2 |
+
{{- '<|im_start|>system\n' }}
|
| 3 |
+
{%- if messages[0]['role'] == 'system' %}
|
| 4 |
+
{{- messages[0]['content'] }}
|
| 5 |
+
{%- else %}
|
| 6 |
+
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
|
| 7 |
+
{%- endif %}
|
| 8 |
+
{{- "\n\n# 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>" }}
|
| 9 |
+
{%- for tool in tools %}
|
| 10 |
+
{{- "\n" }}
|
| 11 |
+
{{- tool | tojson }}
|
| 12 |
+
{%- endfor %}
|
| 13 |
+
{{- "\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" }}
|
| 14 |
+
{%- else %}
|
| 15 |
+
{%- if messages[0]['role'] == 'system' %}
|
| 16 |
+
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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| 17 |
+
{%- else %}
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| 18 |
+
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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| 19 |
+
{%- endif %}
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| 20 |
+
{%- endif %}
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| 21 |
+
{%- for message in messages %}
|
| 22 |
+
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
| 23 |
+
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
| 24 |
+
{%- elif message.role == "assistant" %}
|
| 25 |
+
{{- '<|im_start|>' + message.role }}
|
| 26 |
+
{%- if message.content %}
|
| 27 |
+
{{- '\n' + message.content }}
|
| 28 |
+
{%- endif %}
|
| 29 |
+
{%- for tool_call in message.tool_calls %}
|
| 30 |
+
{%- if tool_call.function is defined %}
|
| 31 |
+
{%- set tool_call = tool_call.function %}
|
| 32 |
+
{%- endif %}
|
| 33 |
+
{{- '\n<tool_call>\n{"name": "' }}
|
| 34 |
+
{{- tool_call.name }}
|
| 35 |
+
{{- '", "arguments": ' }}
|
| 36 |
+
{{- tool_call.arguments | tojson }}
|
| 37 |
+
{{- '}\n</tool_call>' }}
|
| 38 |
+
{%- endfor %}
|
| 39 |
+
{{- '<|im_end|>\n' }}
|
| 40 |
+
{%- elif message.role == "tool" %}
|
| 41 |
+
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
| 42 |
+
{{- '<|im_start|>user' }}
|
| 43 |
+
{%- endif %}
|
| 44 |
+
{{- '\n<tool_response>\n' }}
|
| 45 |
+
{{- message.content }}
|
| 46 |
+
{{- '\n</tool_response>' }}
|
| 47 |
+
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
| 48 |
+
{{- '<|im_end|>\n' }}
|
| 49 |
+
{%- endif %}
|
| 50 |
+
{%- endif %}
|
| 51 |
+
{%- endfor %}
|
| 52 |
+
{%- if add_generation_prompt %}
|
| 53 |
+
{{- '<|im_start|>assistant\n' }}
|
| 54 |
+
{%- endif %}
|
loss.txt
ADDED
|
@@ -0,0 +1 @@
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|
| 1 |
+
8,-0.56
|
special_tokens_map.json
CHANGED
|
@@ -14,13 +14,6 @@
|
|
| 14 |
"<|image_pad|>",
|
| 15 |
"<|video_pad|>"
|
| 16 |
],
|
| 17 |
-
"bos_token": {
|
| 18 |
-
"content": "<|im_end|>",
|
| 19 |
-
"lstrip": false,
|
| 20 |
-
"normalized": false,
|
| 21 |
-
"rstrip": false,
|
| 22 |
-
"single_word": false
|
| 23 |
-
},
|
| 24 |
"eos_token": {
|
| 25 |
"content": "<|im_end|>",
|
| 26 |
"lstrip": false,
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|
| 14 |
"<|image_pad|>",
|
| 15 |
"<|video_pad|>"
|
| 16 |
],
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| 17 |
"eos_token": {
|
| 18 |
"content": "<|im_end|>",
|
| 19 |
"lstrip": false,
|
tokenizer_config.json
CHANGED
|
@@ -194,7 +194,7 @@
|
|
| 194 |
"<|image_pad|>",
|
| 195 |
"<|video_pad|>"
|
| 196 |
],
|
| 197 |
-
"bos_token":
|
| 198 |
"clean_up_tokenization_spaces": false,
|
| 199 |
"eos_token": "<|im_end|>",
|
| 200 |
"errors": "replace",
|
|
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|
| 194 |
"<|image_pad|>",
|
| 195 |
"<|video_pad|>"
|
| 196 |
],
|
| 197 |
+
"bos_token": null,
|
| 198 |
"clean_up_tokenization_spaces": false,
|
| 199 |
"eos_token": "<|im_end|>",
|
| 200 |
"errors": "replace",
|
trainer_state.json
ADDED
|
@@ -0,0 +1,354 @@
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|
| 1 |
+
{
|
| 2 |
+
"best_global_step": null,
|
| 3 |
+
"best_metric": null,
|
| 4 |
+
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 2.0,
|
| 6 |
+
"eval_steps": 500,
|
| 7 |
+
"global_step": 8,
|
| 8 |
+
"is_hyper_param_search": false,
|
| 9 |
+
"is_local_process_zero": true,
|
| 10 |
+
"is_world_process_zero": true,
|
| 11 |
+
"log_history": [
|
| 12 |
+
{
|
| 13 |
+
"clip_ratio/high_max": 0.0,
|
| 14 |
+
"clip_ratio/high_mean": 0.0,
|
| 15 |
+
"clip_ratio/low_mean": 0.0,
|
| 16 |
+
"clip_ratio/low_min": 0.0,
|
| 17 |
+
"clip_ratio/region_mean": 0.0,
|
| 18 |
+
"completions/clipped_ratio": 0.0,
|
| 19 |
+
"completions/max_length": 3.0,
|
| 20 |
+
"completions/max_terminated_length": 3.0,
|
| 21 |
+
"completions/mean_length": 2.407738208770752,
|
| 22 |
+
"completions/mean_terminated_length": 2.407738208770752,
|
| 23 |
+
"completions/min_length": 2.0,
|
| 24 |
+
"completions/min_terminated_length": 2.0,
|
| 25 |
+
"entropy": 0.7712807655334473,
|
| 26 |
+
"epoch": 0.25,
|
| 27 |
+
"frac_reward_zero_std": 0.5297619104385376,
|
| 28 |
+
"grad_norm": 4.7248921394348145,
|
| 29 |
+
"learning_rate": 0.0,
|
| 30 |
+
"loss": 0.0255,
|
| 31 |
+
"num_tokens": 117737.0,
|
| 32 |
+
"reward": 0.4955357313156128,
|
| 33 |
+
"reward_std": 0.4984852075576782,
|
| 34 |
+
"rewards/env_game_reward/mean": 0.4955357015132904,
|
| 35 |
+
"rewards/env_game_reward/std": 0.4984852075576782,
|
| 36 |
+
"sampling/importance_sampling_ratio/max": 2.444530487060547,
|
| 37 |
+
"sampling/importance_sampling_ratio/mean": 0.9743361473083496,
|
| 38 |
+
"sampling/importance_sampling_ratio/min": 0.3661190867424011,
|
| 39 |
+
"sampling/sampling_logp_difference/max": 0.8435878753662109,
|
| 40 |
+
"sampling/sampling_logp_difference/mean": 0.10781625658273697,
|
| 41 |
+
"step": 1,
|
| 42 |
+
"step_time": 86.73120934999952
|
| 43 |
+
},
|
| 44 |
+
{
|
| 45 |
+
"clip_ratio/high_max": 0.0,
|
| 46 |
+
"clip_ratio/high_mean": 0.0,
|
| 47 |
+
"clip_ratio/low_mean": 0.0,
|
| 48 |
+
"clip_ratio/low_min": 0.0,
|
| 49 |
+
"clip_ratio/region_mean": 0.0,
|
| 50 |
+
"completions/clipped_ratio": 0.0,
|
| 51 |
+
"completions/max_length": 41.0,
|
| 52 |
+
"completions/max_terminated_length": 41.0,
|
| 53 |
+
"completions/mean_length": 2.5892858505249023,
|
| 54 |
+
"completions/mean_terminated_length": 2.5892858505249023,
|
| 55 |
+
"completions/min_length": 2.0,
|
| 56 |
+
"completions/min_terminated_length": 2.0,
|
| 57 |
+
"entropy": 0.6681132614612579,
|
| 58 |
+
"epoch": 0.5,
|
| 59 |
+
"frac_reward_zero_std": 0.3988095223903656,
|
| 60 |
+
"grad_norm": 2.0864083766937256,
|
| 61 |
+
"learning_rate": 2.84304e-07,
|
| 62 |
+
"loss": 0.0384,
|
| 63 |
+
"num_tokens": 222599.0,
|
| 64 |
+
"reward": 0.5297619104385376,
|
| 65 |
+
"reward_std": 0.4908183515071869,
|
| 66 |
+
"rewards/env_game_reward/mean": 0.5297619104385376,
|
| 67 |
+
"rewards/env_game_reward/std": 0.4908183515071869,
|
| 68 |
+
"sampling/importance_sampling_ratio/max": 2.3313705921173096,
|
| 69 |
+
"sampling/importance_sampling_ratio/mean": 0.9879763126373291,
|
| 70 |
+
"sampling/importance_sampling_ratio/min": 0.0,
|
| 71 |
+
"sampling/sampling_logp_difference/max": 1.0128240585327148,
|
| 72 |
+
"sampling/sampling_logp_difference/mean": 0.09322669357061386,
|
| 73 |
+
"step": 2,
|
| 74 |
+
"step_time": 49.185698066999976
|
| 75 |
+
},
|
| 76 |
+
{
|
| 77 |
+
"clip_ratio/high_max": 0.010094599798321724,
|
| 78 |
+
"clip_ratio/high_mean": 0.005047299899160862,
|
| 79 |
+
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