Instructions to use User01110/testing-50M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use User01110/testing-50M with Transformers:

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

pipe = pipeline("text-generation", model="User01110/testing-50M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("User01110/testing-50M")
model = AutoModelForCausalLM.from_pretrained("User01110/testing-50M")
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 Settings

vLLM

How to use User01110/testing-50M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "User01110/testing-50M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "User01110/testing-50M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/User01110/testing-50M

SGLang

How to use User01110/testing-50M 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 "User01110/testing-50M" \
    --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": "User01110/testing-50M",
		"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 "User01110/testing-50M" \
        --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": "User01110/testing-50M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use User01110/testing-50M with Docker Model Runner:
```
docker model run hf.co/User01110/testing-50M
```

User01110 commited on 3 days ago

Commit

6b49457

verified ·

1 Parent(s): d821e28

Upload checkpoint step 1,000

Browse files

Files changed (4) hide show

README.md +21 -35
config.json +1 -1
generation_config.json +1 -1
model.safetensors +1 -1

README.md CHANGED Viewed

@@ -8,20 +8,15 @@ base_model: SupraLabs/Supra-1.5-50M-Base-exp
 base_model_relation: finetune
 datasets:
 - nvidia/Nemotron-SFT-Instruction-Following-Chat-v2
-- Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned
-- MBZUAI/LaMini-instruction
-- ketchup123/tulu-gsm8k-openmath-instruct-100k-LF
-- NecroMOnk/khan-math-linear_algebra
-- endurasolution/ron-math-dataset
-- User01110/math-curated-dataset
 - microsoft/orca-math-word-problems-200k
 - TIGER-Lab/MathInstruct
-- openai/gsm8k
-- EleutherAI/arithmetic
 - Programming-Language/codeagent-python
-- jan-hq/multiturn_programming_binarized
 - Cutecat6152/python-data-basic
 - flytech/python-codes-25k
 tags:
 - sft
 - exact-loss-trainer
@@ -44,11 +39,11 @@ This is an experimental instruction SFT run from `SupraLabs/Supra-1.5-50M-Base-e
 | Base revision | `main` |
 | Output repo | `User01110/testing-50M` |
 | Sequence length | 1024 |
-| Max optimizer steps | 20,000 |
 | Per-device batch size | 128 |
 | Gradient accumulation | 4 |
-| Sample presentations per GPU | 10,240,000 |
-| Max token slots per GPU | 10,485,760,000 |
 | Learning rate | 2.00e-04 |
 | Warmup steps | 100 |
 | Weight decay | 0.05 |
@@ -59,9 +54,9 @@ This is an experimental instruction SFT run from `SupraLabs/Supra-1.5-50M-Base-e
 | Prompt format | ChatML |
 | System prompt | `You are a helpful assistant.` |
-The stream randomly mixes math, coding, and conversation-heavy instruction sources. Sources are reopened after exhaustion and keep relooping until the 20,000-step training cap finishes.
-Listed source rows before relooping: 35,728,143. The 20,000-step training budget presents 10,240,000 examples per GPU.
 ## Prompt Template Compatibility
@@ -129,30 +124,21 @@ print(text)
 | Dataset | Config | Split | Rows | Schema | Mapping | Pass policy |
 | --- | --- | --- | ---: | --- | --- | --- |
-| nvidia/Nemotron-SFT-Instruction-Following-Chat-v2 | default | reasoning_off | 1,068,273 | messages[{role, content}], uuid, license, used_in, reasoning | ChatML conversation turns; reasoning_off split only | reloops until max_steps |
-| Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned | General-Distillation | train | 187,794 | conversations[{from, value}], input, output, domain, meta | human/gpt turns; assistant <think> blocks stripped | reloops until max_steps |
-| Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned | General-Math | train | 76,727 | conversations[{from, value}], input, output, domain, meta | human/gpt turns; assistant <think> blocks stripped | reloops until max_steps |
-| Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned | MultilingualSTEM | train | 89,997 | conversations[{from, value}], input, output, domain, meta | human/gpt turns; assistant <think> blocks stripped | reloops until max_steps |
-| Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned | PHD-Science | train | 103,307 | conversations[{from, value}], input, output, domain, meta | human/gpt turns; assistant <think> blocks stripped | reloops until max_steps |
-| MBZUAI/LaMini-instruction | default | train | 2,585,615 | instruction, response, instruction_source | instruction -> response | reloops until max_steps |
-| ketchup123/tulu-gsm8k-openmath-instruct-100k-LF | default | train | 100,000 | conversations[{role, content}] | math conversations to ChatML turns | reloops until max_steps |
-| NecroMOnk/khan-math-linear_algebra | default | train | 1,295,000 | messages[{role, content}], topic, subtopic | math tutor messages to ChatML turns | reloops until max_steps |
-| endurasolution/ron-math-dataset | default | train | 29,226,764 | instruction, input, output | instruction + optional input -> output | reloops until max_steps |
-| User01110/math-curated-dataset | default | train | 50,944 | id, source, prompt, index, model, response, chatml | prompt -> response; ignores source ChatML column and rebuilds clean ChatML | reloops until max_steps |
-| microsoft/orca-math-word-problems-200k | default | train | 200,035 | question, answer | question -> answer | reloops until max_steps |
-| TIGER-Lab/MathInstruct | default | train | 262,039 | source, instruction, output | instruction -> output | reloops until max_steps |
-| openai/gsm8k | main | train | 7,473 | question, answer | question -> answer | reloops until max_steps |
-| openai/gsm8k | socratic | train | 7,473 | question, answer | question -> answer | reloops until max_steps |
-| EleutherAI/arithmetic | 10 validation subsets | validation | 20,000 | context, completion | direct parquet URLs to avoid dataset-script loader failure | reloops until max_steps |
-| Programming-Language/codeagent-python | default | train | 296,837 | prompt, response | prompt -> response | reloops until max_steps |
-| jan-hq/multiturn_programming_binarized | default | train | 100,139 | messages[{role, content}] | single/multiturn programming messages; all assistant spans labeled | reloops until max_steps |
-| Cutecat6152/python-data-basic | default | train | 100 | id, instruction, response | instruction -> response | reloops until max_steps |
-| flytech/python-codes-25k | default | train | 49,626 | instruction, input, output, text | instruction + optional input -> output | reloops until max_steps |
 ## Notes
 - Dataset schemas and row counts were checked through Hugging Face Dataset Viewer metadata where available.
 - Multiturn/message datasets carry all assistant spans into the collator, so user/system text remains masked from step 0 while every assistant turn is supervised.
-- Kimi assistant text has `<think>...</think>` blocks stripped before tokenization.
-- Streaming source open/read failures are retried and reopened. Normal stream exhaustion reopens that source and continues mixing it until `max_steps`.
 - RoPE buffers and tokenizer/model load are verified during final export.

 base_model_relation: finetune
 datasets:
 - nvidia/Nemotron-SFT-Instruction-Following-Chat-v2
 - microsoft/orca-math-word-problems-200k
 - TIGER-Lab/MathInstruct
+- User01110/math-curated-dataset
 - Programming-Language/codeagent-python
 - Cutecat6152/python-data-basic
 - flytech/python-codes-25k
+- QuixiAI/open-instruct-uncensored
+- openai/gsm8k
+- EleutherAI/arithmetic
 tags:
 - sft
 - exact-loss-trainer
 | Base revision | `main` |
 | Output repo | `User01110/testing-50M` |
 | Sequence length | 1024 |
+| Max optimizer steps | 10,000 |
 | Per-device batch size | 128 |
 | Gradient accumulation | 4 |
+| Sample presentations per GPU | 5,120,000 |
+| Max token slots per GPU | 5,242,880,000 |
 | Learning rate | 2.00e-04 |
 | Warmup steps | 100 |
 | Weight decay | 0.05 |
 | Prompt format | ChatML |
 | System prompt | `You are a helpful assistant.` |
+The stream randomly mixes the selected instruction, math, and coding sources. Sources are reopened after exhaustion and keep relooping until the 10,000-step training cap finishes, except `Cutecat6152/python-data-basic`, which is capped at 3 passes.
+Listed source rows before relooping: 3,718,915. The 10,000-step training budget presents 5,120,000 examples per GPU.
 ## Prompt Template Compatibility
 | Dataset | Config | Split | Rows | Schema | Mapping | Pass policy |
 | --- | --- | --- | ---: | --- | --- | --- |
+| nvidia/Nemotron-SFT-Instruction-Following-Chat-v2 | default | reasoning_off | 1,068,273 | messages[{role, content, reasoning_content}] | user/assistant message pairs; reasoning_off only | reloops until max_steps |
+| microsoft/orca-math-word-problems-200k | default | train | 200,035 | question, answer | user=question; assistant=answer | reloops until max_steps |
+| TIGER-Lab/MathInstruct | default | train | 262,039 | source, instruction, output | user=instruction; assistant=output | reloops until max_steps |
+| User01110/math-curated-dataset | default | train | 50,944 | id, source, prompt, index, model, response, chatml | user=prompt; assistant=response; rebuilds clean ChatML | reloops until max_steps |
+| Programming-Language/codeagent-python | default | train | 296,837 | prompt, response | user=prompt; assistant=response | reloops until max_steps |
+| Cutecat6152/python-data-basic | default | train | 100 | id, instruction, response | user=instruction; assistant=response | max 3 passes, 300 presentations max |
+| flytech/python-codes-25k | default | train | 49,626 | instruction, input, output, text | user=instruction plus optional Input block; assistant=output | reloops until max_steps |
+| QuixiAI/open-instruct-uncensored | default | train | 1,756,115 | dataset, id, messages[{role, content}] | user/assistant message pairs | reloops until max_steps |
+| openai/gsm8k | main | train | 7,473 | question, answer | user=question; assistant=answer | reloops until max_steps |
+| openai/gsm8k | socratic | train | 7,473 | question, answer | user=question; assistant=answer | reloops until max_steps |
+| EleutherAI/arithmetic | 10 validation subsets | validation raw JSONL | 20,000 | context, completion | user=context with trailing Answer: stripped; assistant=completion | reloops until max_steps |
 ## Notes
 - Dataset schemas and row counts were checked through Hugging Face Dataset Viewer metadata where available.
 - Multiturn/message datasets carry all assistant spans into the collator, so user/system text remains masked from step 0 while every assistant turn is supervised.
+- Streaming source open/read failures are retried and reopened. Normal stream exhaustion reopens that source and continues mixing it until `max_steps`; `python-data-basic` is dropped after 3 completed passes.
 - RoPE buffers and tokenizer/model load are verified during final export.

config.json CHANGED Viewed

@@ -28,7 +28,7 @@
     "type": "linear"
   },
   "tie_word_embeddings": true,
-  "transformers_version": "5.10.2",
   "use_cache": false,
   "vocab_size": 32002
 }

     "type": "linear"
   },
   "tie_word_embeddings": true,
+  "transformers_version": "5.12.0",
   "use_cache": false,
   "vocab_size": 32002
 }

generation_config.json CHANGED Viewed

@@ -5,5 +5,5 @@
     2
   ],
   "pad_token_id": 1,
-  "transformers_version": "5.10.2"
 }

     2
   ],
   "pad_token_id": 1,
+  "transformers_version": "5.12.0"
 }

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
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