Instructions to use gocharlie-ai/charlie-1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gocharlie-ai/charlie-1.5 with Transformers:

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

pipe = pipeline("text-generation", model="gocharlie-ai/charlie-1.5")
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("gocharlie-ai/charlie-1.5")
model = AutoModelForCausalLM.from_pretrained("gocharlie-ai/charlie-1.5", 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

vLLM

How to use gocharlie-ai/charlie-1.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gocharlie-ai/charlie-1.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gocharlie-ai/charlie-1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gocharlie-ai/charlie-1.5

SGLang

How to use gocharlie-ai/charlie-1.5 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 "gocharlie-ai/charlie-1.5" \
    --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": "gocharlie-ai/charlie-1.5",
		"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 "gocharlie-ai/charlie-1.5" \
        --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": "gocharlie-ai/charlie-1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gocharlie-ai/charlie-1.5 with Docker Model Runner:
```
docker model run hf.co/gocharlie-ai/charlie-1.5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Charlie 1.5

Charlie 1.5 is a high-performance, 12B-parameter large language model (LLM) built on the Mistral architecture. It is designed for long-context reasoning, complex enterprise workflows, and structured decision-making.

With a 131,072-token (128k+) context window, Charlie 1.5 enables native processing of large inputs, such as financial filings, legal contracts, technical reports without requiring retrieval-augmented generation (RAG) pipelines or external chunking systems.

Model Summary

Attribute	Description
Architecture	Mistral-based decoder-only transformer
Parameters	~12B
Layers	40
Hidden Size	5,120
Context Window	131,072 tokens
Vocabulary Size	131,072
Precision	bfloat16 (BF16)
License	Apache License 2.0

Model Highlights

Extended Context: Native support for 131k-token sequences using RoPE (theta: 1,000,000)
Efficient Attention: Grouped Query Attention (32 attention heads, 8 KV heads)
Broad Coverage: Large vocabulary supporting multilingual, technical, and domain-specific text
Deployment-Friendly: Optimized for mid-range GPUs such as NVIDIA A10G
Long-Form Reasoning: Particularly effective on large-document and multi-step reasoning tasks

Performance & Benchmarks

Benchmark	Score
MMLU	68
MMLU-Pro	39
ARC-Challenge	60

Inference Performance (NVIDIA A10G)

Time to First Token (TTFT): ~80 ms
Throughput: ~146 tokens/sec
Precision: bfloat16 (BF16)

Benchmark results are indicative and may vary depending on hardware, prompt length, and configuration.

Intended Use & Scope

Charlie 1.5 is intended for:

Long-context document analysis
Enterprise decision-support systems
Research and experimentation
Commercial and non-commercial applications
Fine-tuning and derivative model development

The model is provided as-is and should be independently evaluated before use in high-risk or safety-critical applications.

Usage

Charlie 1.5 can be used with the Hugging Face transformers library:

import torch
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="your-username/charlie-1.5",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

prompt = """
Analyze the impact of a 15% tariff increase on lithium-ion components from the Asia-Pacific region. 

1. Identify the top 3 Tier 2 suppliers most at risk based on current lead times.
2. Propose a diversification strategy for our European assembly plant.
3. Calculate the projected shift in COGS if we pivot 40% of sourcing to Mexico.
"""
messages = [
     {"role": "system", "content": ""},
     {"role": "user", "content": prompt},
]

outputs = pipe(messages,
              max_new_tokens=512,
              do_sample=True,
              temperature=0.1,
              use_cache=True,
              return_full_text=False,
              num_return_sequences=1
              )
for output in outputs:
    print(output['generated_text'])

Technical Specifications

Hidden Size: 5,120
Intermediate Size: 14,336
Attention Heads: 32 (8 KV heads using Grouped Query Attention)
Activation Function: SiLU
Normalization: RMSNorm (epsilon: 1e-05)
Max Position Embeddings: 131,072

License

The model is released freely and without restriction under the Apache License 2.0. There are no restrictions on downstream usage beyond those stated in the license.

Citation & Attribution

If you use Charlie 1.5 in research or commercial applications, please attribute it to the original Gaudium AI development team.

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Safetensors

Model size

12B params

Tensor type

BF16

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Quantizations

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