Instructions to use dirac-run/ec-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dirac-run/ec-0.6b with Transformers:

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

pipe = pipeline("text-generation", model="dirac-run/ec-0.6b")
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("dirac-run/ec-0.6b")
model = AutoModelForCausalLM.from_pretrained("dirac-run/ec-0.6b", 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 dirac-run/ec-0.6b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dirac-run/ec-0.6b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dirac-run/ec-0.6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dirac-run/ec-0.6b

SGLang

How to use dirac-run/ec-0.6b 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 "dirac-run/ec-0.6b" \
    --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": "dirac-run/ec-0.6b",
		"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 "dirac-run/ec-0.6b" \
        --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": "dirac-run/ec-0.6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dirac-run/ec-0.6b with Docker Model Runner:
```
docker model run hf.co/dirac-run/ec-0.6b
```

ec-0.6b / training.json

dirac-run

Publish selected EasyCommand checkpoint

79f6380 verified 21 days ago

Raw History Blame Contribute Delete

2.86 kB

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    "revision": "c1899de289a04d12100db370d81485cdf75e47ca",
    "parameter_count": 596049920
  },
  "selected_checkpoint": "A1",
  "parent_checkpoint_step": 14833,
  "parent_training": {
    "seed": 42,
    "effective_batch": 32,
    "rank": 32,
    "alpha": 64,
    "dropout": 0.05,
    "target_modules": [
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      "k_proj",
      "v_proj",
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    ],
    "rows": 474635,
    "peak_learning_rate": 0.0002,
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    "optimizer": "AdamW",
    "betas": [
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    ],
    "epsilon": 1e-08,
    "weight_decay": 0.0,
    "schedule": "linear warmup then cosine decay",
    "base_dtype": "bfloat16",
    "trainable_adapter_dtype": "float32",
    "max_sequence_tokens": 2048,
    "assistant_only_loss": true,
    "length_bucketed": true,
    "prompt_file": "training-system-prompt.txt",
    "prompt_sha256": "80e46c8e196e108ea50420d1c7b0b28d9aa07a2df1ccbf49ebd63d466c4a37f5",
    "epochs": 1,
    "example_order": "shuffled length buckets; one complete weighted epoch"
  },
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    "rank": 32,
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    "target_modules": [
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    "rows": 2564,
    "peak_learning_rate": 5e-06,
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    "repair_mix_fraction": 0.5,
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  },
  "training_data_release": "dirac-run/ec-training-data",
  "data_release_is_deduplicated_union_not_exact_weighted_epoch": true,
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  "full_fidelity_artifacts_in_this_repository": true,
  "merged_dtype": "bfloat16",
  "adapter_dtype": "float32",
  "adapter_subfolder": "adapter",
  "optimizer_and_rng_state_included": false,
  "merge": "FP32(original BF16 base) + (alpha/r) * FP32(B @ A), then BF16 rounding",
  "gguf_repository": "dirac-run/ec-0.6b-gguf",
  "source_adapter_sha256": "b437a3915987229f36143672073321209f9ee466eb18b654dcb61c5839d42683",
  "source_fp32_merge_sha256": "b90ae1f9db878f339f7f6b669c3f7f61bb042e33af1b1792c33e2dd5eb9867f9"
}

	{
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	"repository": "Qwen/Qwen3-0.6B",
	"revision": "c1899de289a04d12100db370d81485cdf75e47ca",
	"parameter_count": 596049920
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	"selected_checkpoint": "A1",
	"parent_checkpoint_step": 14833,
	"parent_training": {
	"seed": 42,
	"effective_batch": 32,
	"rank": 32,
	"alpha": 64,
	"dropout": 0.05,
	"target_modules": [
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	"v_proj",
	"o_proj",
	"gate_proj",
	"up_proj",
	"down_proj"
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	"peak_learning_rate": 0.0002,
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	"optimizer": "AdamW",
	"betas": [
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	0.999
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	"epsilon": 1e-08,
	"weight_decay": 0.0,
	"schedule": "linear warmup then cosine decay",
	"base_dtype": "bfloat16",
	"trainable_adapter_dtype": "float32",
	"max_sequence_tokens": 2048,
	"assistant_only_loss": true,
	"length_bucketed": true,
	"prompt_file": "training-system-prompt.txt",
	"prompt_sha256": "80e46c8e196e108ea50420d1c7b0b28d9aa07a2df1ccbf49ebd63d466c4a37f5",
	"epochs": 1,
	"example_order": "shuffled length buckets; one complete weighted epoch"
	},
	"continuation": {
	"seed": 23042,
	"effective_batch": 32,
	"rank": 32,
	"alpha": 64,
	"dropout": 0.05,
	"target_modules": [
	"q_proj",
	"k_proj",
	"v_proj",
	"o_proj",
	"gate_proj",
	"up_proj",
	"down_proj"
	],
	"rows": 2564,
	"peak_learning_rate": 5e-06,
	"updates": 200,
	"warmup_updates": 10,
	"optimizer": "AdamW",
	"betas": [
	0.9,
	0.999
	],
	"epsilon": 1e-08,
	"weight_decay": 0.0,
	"schedule": "linear warmup then cosine decay",
	"base_dtype": "bfloat16",
	"trainable_adapter_dtype": "float32",
	"max_sequence_tokens": 2048,
	"assistant_only_loss": true,
	"length_bucketed": false,
	"prompt_file": "system-prompt.txt",
	"prompt_sha256": "3a9028d5aebb73c3ed7363e63eb3e751689218775ea5572ab0dc6807e522238b",
	"repair_mix_fraction": 0.5,
	"example_order": "seeded family-aware shuffle; repair/replay cohorts without replacement within cycles"
	},
	"training_data_release": "dirac-run/ec-training-data",
	"data_release_is_deduplicated_union_not_exact_weighted_epoch": true,
	"training_examples_from_other_repairs_also_in_data_release": true,
	"full_fidelity_artifacts_in_this_repository": true,
	"merged_dtype": "bfloat16",
	"adapter_dtype": "float32",
	"adapter_subfolder": "adapter",
	"optimizer_and_rng_state_included": false,
	"merge": "FP32(original BF16 base) + (alpha/r) * FP32(B @ A), then BF16 rounding",
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	}