Instructions to use eshmoideas/open-dev-gen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eshmoideas/open-dev-gen with Transformers:

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

pipe = pipeline("text-generation", model="eshmoideas/open-dev-gen")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("eshmoideas/open-dev-gen")
model = AutoModelForCausalLM.from_pretrained("eshmoideas/open-dev-gen")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use eshmoideas/open-dev-gen with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/eshmoideas/open-dev-gen

SGLang

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

Docker Model Runner
How to use eshmoideas/open-dev-gen with Docker Model Runner:
```
docker model run hf.co/eshmoideas/open-dev-gen
```

open-dev-gen / config.json

eshmoideas

Duplicate from openai/gpt-oss-120b

8b71769 5 days ago

raw

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2.09 kB

	{
	"architectures": [
	"GptOssForCausalLM"
	],
	"attention_bias": true,
	"attention_dropout": 0.0,
	"eos_token_id": 200002,
	"experts_per_token": 4,
	"head_dim": 64,
	"hidden_act": "silu",
	"hidden_size": 2880,
	"initial_context_length": 4096,
	"initializer_range": 0.02,
	"intermediate_size": 2880,
	"layer_types": [
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention"
	],
	"max_position_embeddings": 131072,
	"model_type": "gpt_oss",
	"num_attention_heads": 64,
	"num_experts_per_tok": 4,
	"num_hidden_layers": 36,
	"num_key_value_heads": 8,
	"num_local_experts": 128,
	"output_router_logits": false,
	"pad_token_id": 199999,
	"quantization_config": {
	"modules_to_not_convert": [
	"model.layers.*.self_attn",
	"model.layers.*.mlp.router",
	"model.embed_tokens",
	"lm_head"
	],
	"quant_method": "mxfp4"
	},
	"rms_norm_eps": 1e-05,
	"rope_scaling": {
	"beta_fast": 32.0,
	"beta_slow": 1.0,
	"factor": 32.0,
	"original_max_position_embeddings": 4096,
	"rope_type": "yarn",
	"truncate": false
	},
	"rope_theta": 150000,
	"router_aux_loss_coef": 0.9,
	"sliding_window": 128,
	"swiglu_limit": 7.0,
	"tie_word_embeddings": false,
	"transformers_version": "4.55.0.dev0",
	"use_cache": true,
	"vocab_size": 201088
	}