Instructions to use HuggingFaceBio/Carbon-3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceBio/Carbon-3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="HuggingFaceBio/Carbon-3B-GGUF",
	filename="carbon-3b-bf16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use HuggingFaceBio/Carbon-3B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf HuggingFaceBio/Carbon-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf HuggingFaceBio/Carbon-3B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf HuggingFaceBio/Carbon-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf HuggingFaceBio/Carbon-3B-GGUF: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 HuggingFaceBio/Carbon-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf HuggingFaceBio/Carbon-3B-GGUF: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 HuggingFaceBio/Carbon-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf HuggingFaceBio/Carbon-3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/HuggingFaceBio/Carbon-3B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use HuggingFaceBio/Carbon-3B-GGUF with Ollama:
```
ollama run hf.co/HuggingFaceBio/Carbon-3B-GGUF:Q4_K_M
```

Unsloth Studio

How to use HuggingFaceBio/Carbon-3B-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 HuggingFaceBio/Carbon-3B-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 HuggingFaceBio/Carbon-3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for HuggingFaceBio/Carbon-3B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use HuggingFaceBio/Carbon-3B-GGUF with Docker Model Runner:
```
docker model run hf.co/HuggingFaceBio/Carbon-3B-GGUF:Q4_K_M
```

Lemonade

How to use HuggingFaceBio/Carbon-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull HuggingFaceBio/Carbon-3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Carbon-3B-GGUF-Q4_K_M

List all available models

lemonade list

kashif HF Staff commited on 13 days ago

Commit

35dda16

verified ·

1 Parent(s): 7bb90e8

Cleanup: drop SHA256 column + remove stale Files section

Browse files

Files changed (1) hide show

README.md +7 -12

README.md CHANGED Viewed

@@ -32,13 +32,13 @@ as `<protein_coding_region>` instead of DNA).
 ## Available files
-| Quant | File | Size | SHA256 |
-| --- | --- | --- | --- |
-| BF16 | `carbon-3b-bf16.gguf` | 6.4 GB | `eb4f4273a1498d483b65ecc5ebac419635412988ae00a48490fb089c2ef104c8` |
-| Q8_0 | `carbon-3b-q8_0.gguf` | 3.4 GB | `3a2b2239e8d91bd45fe6452c011f9691657829c08625b239044ebf974f272694` |
-| Q6_K | `carbon-3b-q6_k.gguf` | 2.8 GB | `3635b4802378eb5b4995a27915e0e16ab2726c3cd2bdd1bc39ef6ffa16f9529b` |
-| Q5_K_M | `carbon-3b-q5_k_m.gguf` | 2.4 GB | `ee5c115ce03a77b746259f7b0a946b29fd21666d96207d9d4d2bc303f4a35b48` |
-| Q4_K_M | `carbon-3b-q4_k_m.gguf` | 2.1 GB | `a639a57be601faecbc04f095015bad2bd35cd251f8994b11976f425ab55009e0` |
 K-quants are produced with `--token-embedding-type Q8_0 --output-tensor-type Q8_0`
 so the sensitive embedding/output tensors stay at Q8_0 precision (important for
@@ -56,11 +56,6 @@ git clone https://github.com/ggml-org/llama.cpp
 cd llama.cpp && cmake -B build && cmake --build build -j
 ```
-## Files
-| File | Quant | Size |
-|---|---|---|
-| `carbon-3b-bf16.gguf` | bf16 (lossless from source) | 6.5 GB |
 ## Usage

 ## Available files
+| Quant | File | Size |
+| --- | --- | --- |
+| BF16 | `carbon-3b-bf16.gguf` | 6.4 GB |
+| Q8_0 | `carbon-3b-q8_0.gguf` | 3.4 GB |
+| Q6_K | `carbon-3b-q6_k.gguf` | 2.8 GB |
+| Q5_K_M | `carbon-3b-q5_k_m.gguf` | 2.4 GB |
+| Q4_K_M | `carbon-3b-q4_k_m.gguf` | 2.1 GB |
 K-quants are produced with `--token-embedding-type Q8_0 --output-tensor-type Q8_0`
 so the sensitive embedding/output tensors stay at Q8_0 precision (important for
 cd llama.cpp && cmake -B build && cmake --build build -j
 ```
 ## Usage