Instructions to use pthinc/prettybird_bce_basic_coder_8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pthinc/prettybird_bce_basic_coder_8b with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-coder-7b-instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "pthinc/prettybird_bce_basic_coder_8b")

Transformers

How to use pthinc/prettybird_bce_basic_coder_8b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="pthinc/prettybird_bce_basic_coder_8b")
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("pthinc/prettybird_bce_basic_coder_8b")
model = AutoModelForCausalLM.from_pretrained("pthinc/prettybird_bce_basic_coder_8b", 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 pthinc/prettybird_bce_basic_coder_8b 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 pthinc/prettybird_bce_basic_coder_8b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pthinc/prettybird_bce_basic_coder_8b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf pthinc/prettybird_bce_basic_coder_8b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pthinc/prettybird_bce_basic_coder_8b:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf pthinc/prettybird_bce_basic_coder_8b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf pthinc/prettybird_bce_basic_coder_8b:Q4_K_M

Use Docker

docker model run hf.co/pthinc/prettybird_bce_basic_coder_8b:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use pthinc/prettybird_bce_basic_coder_8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pthinc/prettybird_bce_basic_coder_8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pthinc/prettybird_bce_basic_coder_8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pthinc/prettybird_bce_basic_coder_8b:Q4_K_M

SGLang

How to use pthinc/prettybird_bce_basic_coder_8b 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 "pthinc/prettybird_bce_basic_coder_8b" \
    --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": "pthinc/prettybird_bce_basic_coder_8b",
		"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 "pthinc/prettybird_bce_basic_coder_8b" \
        --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": "pthinc/prettybird_bce_basic_coder_8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use pthinc/prettybird_bce_basic_coder_8b with Ollama:
```
ollama run hf.co/pthinc/prettybird_bce_basic_coder_8b:Q4_K_M
```
Unsloth Desktop

How to use pthinc/prettybird_bce_basic_coder_8b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pthinc/prettybird_bce_basic_coder_8b:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "pthinc/prettybird_bce_basic_coder_8b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use pthinc/prettybird_bce_basic_coder_8b with Docker Model Runner:
```
docker model run hf.co/pthinc/prettybird_bce_basic_coder_8b:Q4_K_M
```

Lemonade

How to use pthinc/prettybird_bce_basic_coder_8b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pthinc/prettybird_bce_basic_coder_8b:Q4_K_M

Run and chat with the model

lemonade run user.prettybird_bce_basic_coder_8b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use pthinc/prettybird_bce_basic_coder_8b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pthinc/prettybird_bce_basic_coder_8b:Q4_K_M

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 pthinc/prettybird_bce_basic_coder_8b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use pthinc/prettybird_bce_basic_coder_8b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pthinc/prettybird_bce_basic_coder_8b:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "pthinc/prettybird_bce_basic_coder_8b:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

prettybird_bce_basic_coder_8b / Modelfile

prometechinc

Trained with Unsloth - Ollama Modelfile

d86ffad verified 11 months ago

Raw History Blame

1.75 kB

FROM qwen2.5-coder-7b-instruct.Q2_K.gguf
TEMPLATE """{{- if .Suffix }}<|fim_prefix|>{{ .Prompt }}<|fim_suffix|>{{ .Suffix }}<|fim_middle|>
{{- else if .Messages }}
{{- if or .System .Tools }}<|im_start|>system
{{- if .System }}
{{ .System }}
{{- end }}
{{- if .Tools }}

# Tools

You may call one or more functions to assist with the user query.

You are provided with function signatures within <tools></tools>:
<tools>
{{- range .Tools }}
{"type": "function", "function": {{ .Function }}}
{{- end }}
</tools>

For each function call, return a json object with function name and arguments within <tool_call></tool_call> with NO other text. Do not include any backticks or ```json.
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>
{{- end }}<|im_end|>
{{ end }}
{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 -}}
{{- if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
{{ else if eq .Role "assistant" }}<|im_start|>assistant
{{ if .Content }}{{ .Content }}
{{- else if .ToolCalls }}<tool_call>
{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
{{ end }}</tool_call>
{{- end }}{{ if not $last }}<|im_end|>
{{ end }}
{{- else if eq .Role "tool" }}<|im_start|>user
<tool_response>
{{ .Content }}
</tool_response><|im_end|>
{{ end }}
{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
{{ end }}
{{- end }}
{{- else }}
{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful assistant."""


	FROM qwen2.5-coder-7b-instruct.Q2_K.gguf
	TEMPLATE """{{- if .Suffix }}<\|fim_prefix\|>{{ .Prompt }}<\|fim_suffix\|>{{ .Suffix }}<\|fim_middle\|>
	{{- else if .Messages }}
	{{- if or .System .Tools }}<\|im_start\|>system
	{{- if .System }}
	{{ .System }}
	{{- end }}
	{{- if .Tools }}

	# Tools

	You may call one or more functions to assist with the user query.

	You are provided with function signatures within <tools></tools>:
	<tools>
	{{- range .Tools }}
	{"type": "function", "function": {{ .Function }}}
	{{- end }}
	</tools>

	For each function call, return a json object with function name and arguments within <tool_call></tool_call> with NO other text. Do not include any backticks or ```json.
	<tool_call>
	{"name": <function-name>, "arguments": <args-json-object>}
	</tool_call>
	{{- end }}<\|im_end\|>
	{{ end }}
	{{- range $i, $_ := .Messages }}
	{{- $last := eq (len (slice $.Messages $i)) 1 -}}
	{{- if eq .Role "user" }}<\|im_start\|>user
	{{ .Content }}<\|im_end\|>
	{{ else if eq .Role "assistant" }}<\|im_start\|>assistant
	{{ if .Content }}{{ .Content }}
	{{- else if .ToolCalls }}<tool_call>
	{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
	{{ end }}</tool_call>
	{{- end }}{{ if not $last }}<\|im_end\|>
	{{ end }}
	{{- else if eq .Role "tool" }}<\|im_start\|>user
	<tool_response>
	{{ .Content }}
	</tool_response><\|im_end\|>
	{{ end }}
	{{- if and (ne .Role "assistant") $last }}<\|im_start\|>assistant
	{{ end }}
	{{- end }}
	{{- else }}
	{{- if .System }}<\|im_start\|>system
	{{ .System }}<\|im_end\|>
	{{ end }}{{ if .Prompt }}<\|im_start\|>user
	{{ .Prompt }}<\|im_end\|>
	{{ end }}<\|im_start\|>assistant
	{{ end }}{{ .Response }}{{ if .Response }}<\|im_end\|>{{ end }}"""
	SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful assistant."""