Instructions to use sabbbbir/qwen-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sabbbbir/qwen-models with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sabbbbir/qwen-models", dtype="auto") - llama-cpp-python
How to use sabbbbir/qwen-models with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="sabbbbir/qwen-models", filename="checkpoints/qwen3_6_35b/Qwen_Qwen3.6-35B-A3B-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use sabbbbir/qwen-models with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf sabbbbir/qwen-models:Q4_K_M # Run inference directly in the terminal: llama-cli -hf sabbbbir/qwen-models:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf sabbbbir/qwen-models:Q4_K_M # Run inference directly in the terminal: llama-cli -hf sabbbbir/qwen-models:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf sabbbbir/qwen-models:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf sabbbbir/qwen-models:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf sabbbbir/qwen-models:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf sabbbbir/qwen-models:Q4_K_M
Use Docker
docker model run hf.co/sabbbbir/qwen-models:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use sabbbbir/qwen-models with Ollama:
ollama run hf.co/sabbbbir/qwen-models:Q4_K_M
- Unsloth Studio new
How to use sabbbbir/qwen-models with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for sabbbbir/qwen-models to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for sabbbbir/qwen-models to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for sabbbbir/qwen-models to start chatting
- Pi new
How to use sabbbbir/qwen-models with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf sabbbbir/qwen-models:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "sabbbbir/qwen-models:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use sabbbbir/qwen-models with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf sabbbbir/qwen-models:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default sabbbbir/qwen-models:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use sabbbbir/qwen-models with Docker Model Runner:
docker model run hf.co/sabbbbir/qwen-models:Q4_K_M
- Lemonade
How to use sabbbbir/qwen-models with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sabbbbir/qwen-models:Q4_K_M
Run and chat with the model
lemonade run user.qwen-models-Q4_K_M
List all available models
lemonade list
Auto-push: step 25
Browse files- checkpoint-20/README.md +209 -0
- checkpoint-20/adapter_config.json +46 -0
- checkpoint-20/adapter_model.safetensors +3 -0
- checkpoint-20/chat_template.jinja +1 -0
- checkpoint-20/optimizer.pt +3 -0
- checkpoint-20/rng_state.pth +3 -0
- checkpoint-20/scheduler.pt +3 -0
- checkpoint-20/tokenizer.json +3 -0
- checkpoint-20/tokenizer_config.json +32 -0
- checkpoint-20/trainer_state.json +62 -0
- checkpoint-20/training_args.bin +3 -0
checkpoint-20/README.md
ADDED
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| 1 |
+
---
|
| 2 |
+
base_model: /root/.cache/huggingface/hub/models--sabbbbir--qwen-models/snapshots/9b7eb4d520a7e8faf3656f7b9936c7fca86ab58a/checkpoints/qwen3_6_27b
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| 3 |
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library_name: peft
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| 4 |
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pipeline_tag: text-generation
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| 5 |
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tags:
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| 6 |
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- base_model:adapter:/root/.cache/huggingface/hub/models--sabbbbir--qwen-models/snapshots/9b7eb4d520a7e8faf3656f7b9936c7fca86ab58a/checkpoints/qwen3_6_27b
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| 7 |
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- lora
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| 8 |
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- sft
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| 9 |
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- transformers
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| 10 |
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- trl
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| 11 |
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---
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| 12 |
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| 13 |
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# Model Card for Model ID
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| 14 |
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<!-- Provide a quick summary of what the model is/does. -->
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| 16 |
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| 17 |
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## Model Details
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### Model Description
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| 22 |
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<!-- Provide a longer summary of what this model is. -->
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| 24 |
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| 25 |
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| 27 |
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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| 29 |
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- **Shared by [optional]:** [More Information Needed]
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| 30 |
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- **Model type:** [More Information Needed]
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| 31 |
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- **Language(s) (NLP):** [More Information Needed]
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| 32 |
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- **License:** [More Information Needed]
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| 33 |
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- **Finetuned from model [optional]:** [More Information Needed]
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| 34 |
+
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| 35 |
+
### Model Sources [optional]
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| 36 |
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| 37 |
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<!-- Provide the basic links for the model. -->
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| 38 |
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| 39 |
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- **Repository:** [More Information Needed]
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| 40 |
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- **Paper [optional]:** [More Information Needed]
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| 41 |
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- **Demo [optional]:** [More Information Needed]
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| 42 |
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| 43 |
+
## Uses
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| 44 |
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| 45 |
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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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| 46 |
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| 47 |
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### Direct Use
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| 48 |
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| 49 |
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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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| 50 |
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| 51 |
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[More Information Needed]
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| 52 |
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| 53 |
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### Downstream Use [optional]
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| 54 |
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|
| 55 |
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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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| 56 |
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| 57 |
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[More Information Needed]
|
| 58 |
+
|
| 59 |
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### Out-of-Scope Use
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| 60 |
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| 61 |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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| 62 |
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| 63 |
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[More Information Needed]
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| 64 |
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| 65 |
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## Bias, Risks, and Limitations
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| 66 |
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| 67 |
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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| 68 |
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| 69 |
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[More Information Needed]
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| 70 |
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| 71 |
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### Recommendations
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| 72 |
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| 73 |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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| 74 |
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| 75 |
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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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| 76 |
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| 77 |
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## How to Get Started with the Model
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| 78 |
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| 79 |
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Use the code below to get started with the model.
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| 81 |
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[More Information Needed]
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## Training Details
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| 84 |
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| 85 |
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### Training Data
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| 86 |
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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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| 93 |
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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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| 104 |
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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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| 117 |
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| 118 |
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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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| 137 |
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#### Summary
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| 139 |
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## Model Examination [optional]
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| 143 |
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| 144 |
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<!-- Relevant interpretability work for the model goes here -->
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| 145 |
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| 146 |
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[More Information Needed]
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| 147 |
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| 148 |
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## Environmental Impact
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| 149 |
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| 150 |
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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 -->
|
| 151 |
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| 152 |
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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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| 153 |
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| 154 |
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- **Hardware Type:** [More Information Needed]
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| 155 |
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- **Hours used:** [More Information Needed]
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| 156 |
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- **Cloud Provider:** [More Information Needed]
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| 157 |
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- **Compute Region:** [More Information Needed]
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| 158 |
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- **Carbon Emitted:** [More Information Needed]
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| 159 |
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| 160 |
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## Technical Specifications [optional]
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| 161 |
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| 162 |
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### Model Architecture and Objective
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| 163 |
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| 164 |
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[More Information Needed]
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| 165 |
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| 166 |
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### Compute Infrastructure
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| 167 |
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| 168 |
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[More Information Needed]
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| 169 |
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| 170 |
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#### Hardware
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| 171 |
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| 172 |
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[More Information Needed]
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| 173 |
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#### Software
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| 175 |
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[More Information Needed]
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| 177 |
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| 178 |
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## Citation [optional]
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| 179 |
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| 180 |
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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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| 181 |
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| 182 |
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**BibTeX:**
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| 183 |
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| 184 |
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[More Information Needed]
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| 185 |
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| 186 |
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**APA:**
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| 187 |
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| 188 |
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[More Information Needed]
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| 189 |
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|
| 190 |
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## Glossary [optional]
|
| 191 |
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|
| 192 |
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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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| 193 |
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| 194 |
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[More Information Needed]
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| 195 |
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| 196 |
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## More Information [optional]
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| 197 |
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[More Information Needed]
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| 199 |
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## Model Card Authors [optional]
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| 201 |
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| 202 |
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[More Information Needed]
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| 203 |
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| 204 |
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## Model Card Contact
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| 205 |
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| 206 |
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[More Information Needed]
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| 207 |
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### Framework versions
|
| 208 |
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| 209 |
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- PEFT 0.18.1
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checkpoint-20/adapter_config.json
ADDED
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{
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| 2 |
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"alora_invocation_tokens": null,
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| 3 |
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"alpha_pattern": {},
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| 4 |
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"arrow_config": null,
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| 5 |
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"auto_mapping": null,
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| 6 |
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"base_model_name_or_path": "/root/.cache/huggingface/hub/models--sabbbbir--qwen-models/snapshots/9b7eb4d520a7e8faf3656f7b9936c7fca86ab58a/checkpoints/qwen3_6_27b",
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| 7 |
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"bias": "none",
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| 8 |
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"corda_config": null,
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| 9 |
+
"ensure_weight_tying": false,
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