Instructions to use jerrycheng233/model5_sft_16bit_gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jerrycheng233/model5_sft_16bit_gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jerrycheng233/model5_sft_16bit_gguf",
	filename="GLM-4.7-Flash.BF16-00002-of-00002.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use jerrycheng233/model5_sft_16bit_gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jerrycheng233/model5_sft_16bit_gguf:BF16
# Run inference directly in the terminal:
llama-cli -hf jerrycheng233/model5_sft_16bit_gguf:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jerrycheng233/model5_sft_16bit_gguf:BF16
# Run inference directly in the terminal:
llama-cli -hf jerrycheng233/model5_sft_16bit_gguf:BF16

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 jerrycheng233/model5_sft_16bit_gguf:BF16
# Run inference directly in the terminal:
./llama-cli -hf jerrycheng233/model5_sft_16bit_gguf:BF16

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 jerrycheng233/model5_sft_16bit_gguf:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jerrycheng233/model5_sft_16bit_gguf:BF16

Use Docker

docker model run hf.co/jerrycheng233/model5_sft_16bit_gguf:BF16

LM Studio
Jan
Ollama
How to use jerrycheng233/model5_sft_16bit_gguf with Ollama:
```
ollama run hf.co/jerrycheng233/model5_sft_16bit_gguf:BF16
```

Unsloth Studio

How to use jerrycheng233/model5_sft_16bit_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 jerrycheng233/model5_sft_16bit_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 jerrycheng233/model5_sft_16bit_gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jerrycheng233/model5_sft_16bit_gguf to start chatting

How to use jerrycheng233/model5_sft_16bit_gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf jerrycheng233/model5_sft_16bit_gguf:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "jerrycheng233/model5_sft_16bit_gguf:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use jerrycheng233/model5_sft_16bit_gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf jerrycheng233/model5_sft_16bit_gguf:BF16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default jerrycheng233/model5_sft_16bit_gguf:BF16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use jerrycheng233/model5_sft_16bit_gguf with Docker Model Runner:
```
docker model run hf.co/jerrycheng233/model5_sft_16bit_gguf:BF16
```

Lemonade

How to use jerrycheng233/model5_sft_16bit_gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jerrycheng233/model5_sft_16bit_gguf:BF16

Run and chat with the model

lemonade run user.model5_sft_16bit_gguf-BF16

List all available models

lemonade list

model5_sft_16bit_gguf / config.json

jerrycheng233

Trained with Unsloth - config

952694f verified 2 months ago

raw

history blame contribute delete

2.4 kB

	{
	"architectures": [
	"Glm4MoeLiteForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"bos_token_id": 0,
	"torch_dtype": "bfloat16",
	"eos_token_id": [
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	"first_k_dense_replace": 1,
	"head_dim": 64,
	"hidden_act": "silu",
	"hidden_size": 2048,
	"initializer_range": 0.02,
	"intermediate_size": 10240,
	"kv_lora_rank": 512,
	"max_position_embeddings": 202752,
	"mlp_layer_types": [
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	"model_name": "unsloth/GLM-4.7-Flash",
	"model_type": "glm4_moe_lite",
	"moe_intermediate_size": 1536,
	"n_group": 1,
	"n_routed_experts": 64,
	"n_shared_experts": 1,
	"norm_topk_prob": true,
	"num_attention_heads": 20,
	"num_experts_per_tok": 4,
	"num_hidden_layers": 47,
	"num_key_value_heads": 20,
	"num_nextn_predict_layers": 1,
	"pad_token_id": 154821,
	"partial_rotary_factor": 1.0,
	"pretraining_tp": 1,
	"q_lora_rank": 768,
	"qk_head_dim": 256,
	"qk_nope_head_dim": 192,
	"qk_rope_head_dim": 64,
	"rms_norm_eps": 1e-05,
	"rope_interleave": true,
	"rope_parameters": {
	"partial_rotary_factor": 1.0,
	"rope_theta": 1000000,
	"rope_type": "default"
	},
	"routed_scaling_factor": 1.8,
	"scoring_func": "sigmoid",
	"tie_word_embeddings": false,
	"topk_group": 1,
	"topk_method": "noaux_tc",
	"unsloth_fixed": true,
	"unsloth_version": "2026.3.17",
	"use_cache": true,
	"v_head_dim": 256,
	"vocab_size": 154880
	}