Instructions to use newsletter/neural-chat-7b-v3-3-Q6_K-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use newsletter/neural-chat-7b-v3-3-Q6_K-GGUF 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 newsletter/neural-chat-7b-v3-3-Q6_K-GGUF:Q6_K # Run inference directly in the terminal: llama cli -hf newsletter/neural-chat-7b-v3-3-Q6_K-GGUF:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf newsletter/neural-chat-7b-v3-3-Q6_K-GGUF:Q6_K # Run inference directly in the terminal: llama cli -hf newsletter/neural-chat-7b-v3-3-Q6_K-GGUF:Q6_K
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 newsletter/neural-chat-7b-v3-3-Q6_K-GGUF:Q6_K # Run inference directly in the terminal: ./llama-cli -hf newsletter/neural-chat-7b-v3-3-Q6_K-GGUF:Q6_K
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 newsletter/neural-chat-7b-v3-3-Q6_K-GGUF:Q6_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf newsletter/neural-chat-7b-v3-3-Q6_K-GGUF:Q6_K
Use Docker
docker model run hf.co/newsletter/neural-chat-7b-v3-3-Q6_K-GGUF:Q6_K
- LM Studio
- Jan
- Ollama
How to use newsletter/neural-chat-7b-v3-3-Q6_K-GGUF with Ollama:
ollama run hf.co/newsletter/neural-chat-7b-v3-3-Q6_K-GGUF:Q6_K
- Unsloth Studio
How to use newsletter/neural-chat-7b-v3-3-Q6_K-GGUF 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 newsletter/neural-chat-7b-v3-3-Q6_K-GGUF 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 newsletter/neural-chat-7b-v3-3-Q6_K-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for newsletter/neural-chat-7b-v3-3-Q6_K-GGUF to start chatting
- Docker Model Runner
How to use newsletter/neural-chat-7b-v3-3-Q6_K-GGUF with Docker Model Runner:
docker model run hf.co/newsletter/neural-chat-7b-v3-3-Q6_K-GGUF:Q6_K
- Lemonade
How to use newsletter/neural-chat-7b-v3-3-Q6_K-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull newsletter/neural-chat-7b-v3-3-Q6_K-GGUF:Q6_K
Run and chat with the model
lemonade run user.neural-chat-7b-v3-3-Q6_K-GGUF-Q6_K
List all available models
lemonade list
- Atomic Chat
base_model: Intel/neural-chat-7b-v3-3
license: apache-2.0
tags:
- LLMs
- mistral
- math
- Intel
- llama-cpp
- gguf-my-repo
model-index:
- name: neural-chat-7b-v3-3
results:
- task:
type: Large Language Model
name: Large Language Model
dataset:
name: meta-math/MetaMathQA
type: meta-math/MetaMathQA
metrics:
- type: ARC (25-shot)
value: 66.89
name: ARC (25-shot)
verified: true
- type: HellaSwag (10-shot)
value: 85.26
name: HellaSwag (10-shot)
verified: true
- type: MMLU (5-shot)
value: 63.07
name: MMLU (5-shot)
verified: true
- type: TruthfulQA (0-shot)
value: 63.01
name: TruthfulQA (0-shot)
verified: true
- type: Winogrande (5-shot)
value: 79.64
name: Winogrande (5-shot)
verified: true
- type: GSM8K (5-shot)
value: 61.11
name: GSM8K (5-shot)
verified: true
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 66.89
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Intel/neural-chat-7b-v3-3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 85.26
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Intel/neural-chat-7b-v3-3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 63.07
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Intel/neural-chat-7b-v3-3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 63.01
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Intel/neural-chat-7b-v3-3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 79.64
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Intel/neural-chat-7b-v3-3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 61.11
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Intel/neural-chat-7b-v3-3
name: Open LLM Leaderboard
newsletter/neural-chat-7b-v3-3-Q6_K-GGUF
This model was converted to GGUF format from Intel/neural-chat-7b-v3-3 using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo newsletter/neural-chat-7b-v3-3-Q6_K-GGUF --hf-file neural-chat-7b-v3-3-q6_k.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo newsletter/neural-chat-7b-v3-3-Q6_K-GGUF --hf-file neural-chat-7b-v3-3-q6_k.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo newsletter/neural-chat-7b-v3-3-Q6_K-GGUF --hf-file neural-chat-7b-v3-3-q6_k.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo newsletter/neural-chat-7b-v3-3-Q6_K-GGUF --hf-file neural-chat-7b-v3-3-q6_k.gguf -c 2048