Instructions to use NeoChen1024/CausalLM_35b-beta-long-GGUF-imatrix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use NeoChen1024/CausalLM_35b-beta-long-GGUF-imatrix 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 NeoChen1024/CausalLM_35b-beta-long-GGUF-imatrix:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NeoChen1024/CausalLM_35b-beta-long-GGUF-imatrix:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NeoChen1024/CausalLM_35b-beta-long-GGUF-imatrix:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NeoChen1024/CausalLM_35b-beta-long-GGUF-imatrix: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 NeoChen1024/CausalLM_35b-beta-long-GGUF-imatrix:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NeoChen1024/CausalLM_35b-beta-long-GGUF-imatrix: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 NeoChen1024/CausalLM_35b-beta-long-GGUF-imatrix:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NeoChen1024/CausalLM_35b-beta-long-GGUF-imatrix:Q4_K_M

Use Docker

docker model run hf.co/NeoChen1024/CausalLM_35b-beta-long-GGUF-imatrix:Q4_K_M

LM Studio
Jan
Ollama
How to use NeoChen1024/CausalLM_35b-beta-long-GGUF-imatrix with Ollama:
```
ollama run hf.co/NeoChen1024/CausalLM_35b-beta-long-GGUF-imatrix:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use NeoChen1024/CausalLM_35b-beta-long-GGUF-imatrix with Docker Model Runner:
```
docker model run hf.co/NeoChen1024/CausalLM_35b-beta-long-GGUF-imatrix:Q4_K_M
```

Lemonade

How to use NeoChen1024/CausalLM_35b-beta-long-GGUF-imatrix with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NeoChen1024/CausalLM_35b-beta-long-GGUF-imatrix:Q4_K_M

Run and chat with the model

lemonade run user.CausalLM_35b-beta-long-GGUF-imatrix-Q4_K_M

List all available models

lemonade list

Atomic Chat

GGUF quants of CausalLM/35b-beta-long, here I have:

IQ1_S  (8.0G, 16.8624 +/- 0.24892, fits into 10GiB VRAM, just for kicks and giggles, not really usable)
IQ1_M  (8.6G, 13.9588 +/- 0.19871, fits into 12GiB VRAM, just for kicks and giggles, not really usable)
IQ2_M  ( 12G, 10.1401 +/- 0.14062, fits into 16GiB VRAM + 6144 context with q4_1 KV cache)
IQ4_XS ( 18G,  9.4489 +/- 0.13005, fits into 24GiB VRAM + 8192 context with q4_1 KV cache, also room for 2048 ubatch)
IQ4_NL ( 19G,  9.4632 +/- 0.13056, fits into 24GiB VRAM + 8192 context with q4_1 KV cache)
Q4_K_M ( 21G,  9.3738 +/- 0.12900, fits into 24GiB VRAM + 6144 context with q4_1 KV cache, also good for CPU inference on E5-26xx v3/v4)
Q8_0   ( 35G,  9.3277 +/- 0.12781, probably isn't practical for anything unless you have big GPU array, imatrix derived from it)

Perplexity measured with -fa -ctv q4_1 -ctk q4_1 -c 2048 -ub 2048 on UTF-8 text version of "Wired Love" from Project Gutenberg.

Downloads last month: 245

GGUF

Model size

35B params

Architecture

command-r

Hardware compatibility

1-bit

2-bit

4-bit

8-bit

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