Instructions to use uavleeva/grpo_merged_math_sql_code_ties_001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use uavleeva/grpo_merged_math_sql_code_ties_001 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-coder-7b-instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "uavleeva/grpo_merged_math_sql_code_ties_001") - Transformers
How to use uavleeva/grpo_merged_math_sql_code_ties_001 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="uavleeva/grpo_merged_math_sql_code_ties_001") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("uavleeva/grpo_merged_math_sql_code_ties_001", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use uavleeva/grpo_merged_math_sql_code_ties_001 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "uavleeva/grpo_merged_math_sql_code_ties_001" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "uavleeva/grpo_merged_math_sql_code_ties_001", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/uavleeva/grpo_merged_math_sql_code_ties_001
- SGLang
How to use uavleeva/grpo_merged_math_sql_code_ties_001 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 "uavleeva/grpo_merged_math_sql_code_ties_001" \ --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": "uavleeva/grpo_merged_math_sql_code_ties_001", "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 "uavleeva/grpo_merged_math_sql_code_ties_001" \ --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": "uavleeva/grpo_merged_math_sql_code_ties_001", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio new
How to use uavleeva/grpo_merged_math_sql_code_ties_001 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 uavleeva/grpo_merged_math_sql_code_ties_001 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 uavleeva/grpo_merged_math_sql_code_ties_001 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for uavleeva/grpo_merged_math_sql_code_ties_001 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="uavleeva/grpo_merged_math_sql_code_ties_001", max_seq_length=2048, ) - Docker Model Runner
How to use uavleeva/grpo_merged_math_sql_code_ties_001 with Docker Model Runner:
docker model run hf.co/uavleeva/grpo_merged_math_sql_code_ties_001
Upload 2 files
Browse files- adapter_config.json +50 -0
- adapter_model.safetensors +3 -0
adapter_config.json
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{
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"alora_invocation_tokens": null,
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"alpha_pattern": {},
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"arrow_config": null,
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"auto_mapping": {
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"base_model_class": "Qwen2ForCausalLM",
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"parent_library": "transformers.models.qwen2.modeling_qwen2",
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"unsloth_fixed": true
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},
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"base_model_name_or_path": "unsloth/qwen2.5-coder-7b-instruct-bnb-4bit",
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"bias": "none",
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"corda_config": null,
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"ensure_weight_tying": false,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 64,
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"lora_bias": false,
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"lora_dropout": 0.0,
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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": 64,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"o_proj",
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"q_proj",
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"up_proj",
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"k_proj",
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"v_proj",
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"gate_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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"trainable_token_indices": null,
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"use_dora": false,
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"use_qalora": false,
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"use_rslora": false
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}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:238fb9b9f048bb866a0a640395478fb2ae5b25bf320713a78ddacb8f0a752a1c
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size 645975704
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