Text Generation
Transformers
Safetensors
GGUF
mistral
mergekit
Merge
conversational
text-generation-inference
Instructions to use NikitosKey/Merge-Math-Coder-7B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NikitosKey/Merge-Math-Coder-7B-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NikitosKey/Merge-Math-Coder-7B-v1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NikitosKey/Merge-Math-Coder-7B-v1") model = AutoModelForCausalLM.from_pretrained("NikitosKey/Merge-Math-Coder-7B-v1", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use NikitosKey/Merge-Math-Coder-7B-v1 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf NikitosKey/Merge-Math-Coder-7B-v1:Q8_0 # Run inference directly in the terminal: llama cli -hf NikitosKey/Merge-Math-Coder-7B-v1:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf NikitosKey/Merge-Math-Coder-7B-v1:Q8_0 # Run inference directly in the terminal: llama cli -hf NikitosKey/Merge-Math-Coder-7B-v1:Q8_0
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 NikitosKey/Merge-Math-Coder-7B-v1:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf NikitosKey/Merge-Math-Coder-7B-v1:Q8_0
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 NikitosKey/Merge-Math-Coder-7B-v1:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf NikitosKey/Merge-Math-Coder-7B-v1:Q8_0
Use Docker
docker model run hf.co/NikitosKey/Merge-Math-Coder-7B-v1:Q8_0
- LM Studio
- Jan
- vLLM
How to use NikitosKey/Merge-Math-Coder-7B-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NikitosKey/Merge-Math-Coder-7B-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NikitosKey/Merge-Math-Coder-7B-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/NikitosKey/Merge-Math-Coder-7B-v1:Q8_0
- SGLang
How to use NikitosKey/Merge-Math-Coder-7B-v1 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 "NikitosKey/Merge-Math-Coder-7B-v1" \ --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": "NikitosKey/Merge-Math-Coder-7B-v1", "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 "NikitosKey/Merge-Math-Coder-7B-v1" \ --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": "NikitosKey/Merge-Math-Coder-7B-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use NikitosKey/Merge-Math-Coder-7B-v1 with Ollama:
ollama run hf.co/NikitosKey/Merge-Math-Coder-7B-v1:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use NikitosKey/Merge-Math-Coder-7B-v1 with Docker Model Runner:
docker model run hf.co/NikitosKey/Merge-Math-Coder-7B-v1:Q8_0
- Lemonade
How to use NikitosKey/Merge-Math-Coder-7B-v1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull NikitosKey/Merge-Math-Coder-7B-v1:Q8_0
Run and chat with the model
lemonade run user.Merge-Math-Coder-7B-v1-Q8_0
List all available models
lemonade list
- Atomic Chat
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Download README.md from NikitosKey/Merge-Math-Coder-7B-v1: direct link, hf CLI and curl.
- Browser
- Download file 1.29 kB
-
https://huggingface.co/NikitosKey/Merge-Math-Coder-7B-v1/resolve/main/README.md
- Command line
-
hf download hf://NikitosKey/Merge-Math-Coder-7B-v1/README.md
-
curl -L -o README.md https://huggingface.co/NikitosKey/Merge-Math-Coder-7B-v1/resolve/main/README.md
1.29 kB
metadata
base_model:
- mistralai/Mistral-7B-Instruct-v0.2
- WizardLM/WizardMath-7B-V1.1
library_name: transformers
tags:
- mergekit
- merge
merged_model
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the TIES merge method using mistralai/Mistral-7B-Instruct-v0.2 as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
models:
- model: mistralai/Mistral-7B-Instruct-v0.2
# Базовая модель, не меняем
parameters:
density: 1.0
weight: 1.0
- model: WizardLM/WizardMath-7B-V1.1
# Эксперт подмешивается
parameters:
density: 0.5 # DARE: берем только 50% самых важных изменений
weight: 0.5 # Вес влияния
merge_method: ties
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
normalize: true
int8_mask: true
dtype: float16