treyleo16/ gpt-2

10 Downloads Updated 1 week ago

GPT -2 Large, made by OpenAI. 0.8B parameters.

ollama run treyleo16/gpt-2

curl http://localhost:11434/api/chat \
  -d '{
    "model": "treyleo16/gpt-2",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='treyleo16/gpt-2',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'treyleo16/gpt-2',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Models

Name

1 model

Size / Usage

Context

Input

gpt-2:latest

549MB · 1K context window · Text · 1 week ago

gpt-2:latest

549MB

1K

Text

Readme

treyleo16/gpt-2

OpenAI’s GPT-2 Large packaged for Ollama as a Q4_K_M GGUF, with a Q&A prompt template so the base model can answer short questions in chat.

ollama run treyleo16/gpt-2

Model details


Base model	GPT-2 Large (OpenAI, 2019)
Architecture	`gpt2`
Parameters	774M (Ollama reports 838M)
Quantization	Q4_K_M
Download size	~550 MB
Context length	1024 tokens
Embedding size	1280
Capabilities	Text completion
License	MIT (inherited from GPT-2)

What it is (and isn’t)

GPT-2 Large is a base model. It was never instruction-tuned or trained on chat data, so it predicts the next text and does not follow instructions. The template here frames each turn as a Q: / A: exchange, and stop sequences cut it off after one answer line, so it gives short answers.

Good for: - Short factual questions and one-line answers - Raw text continuation (story starts, article openings) - Experiments, benchmarks, and learning how prompt templates steer a base model - Running on low-end hardware (~20 tok/s on CPU)

Not good for: - Code: its output is not valid, working code - Multi-step instructions, math, or reasoning - Factual accuracy: it writes smoothly and confidently makes things up - Long answers: output stops at the first newline

Example output

Prompt	Response
Hi, who are you?	I’m a friendly person that helps people.
What is the capital of France?	Paris.
What is 2+2?	Two and two are equal to four.
What color is the sky?	It’s blue!

Raw completion mode

To skip the Q&A template and use GPT-2 as a plain text continuer:

curl http://localhost:11434/api/generate -d '{
  "model": "treyleo16/gpt-2",
  "raw": true,
  "prompt": "Once upon a time in a small mountain town,",
  "options": { "stop": [], "num_predict": 200, "temperature": 0.8 }
}'

Tips

Keep num_ctx at 1024 or lower. GPT-2 can’t handle longer context.
For longer answers, remove the \n stop and keep "Q:". Output gets longer but wanders more.
Lower temperature (0.3–0.5) gives more on-topic answers. Higher (0.8–1.0) works better for creative text.

Credits

Base model: GPT-2 by OpenAI, “Language Models are Unsupervised Multitask Learners” (Radford et al., 2019)
Packaging, template, and tuning: treyleo16

# treyleo16/gpt-2

OpenAI's **GPT-2 Large** packaged for Ollama as a **Q4_K_M GGUF**, with a Q&A prompt template so the base model can answer short questions in chat.

```sh
ollama run treyleo16/gpt-2
```

## Model details

| | |
|---|---|
| Base model | GPT-2 Large (OpenAI, 2019) |
| Architecture | `gpt2` |
| Parameters | 774M (Ollama reports 838M) |
| Quantization | Q4_K_M |
| Download size | ~550 MB |
| Context length | 1024 tokens |
| Embedding size | 1280 |
| Capabilities | Text completion |
| License | MIT (inherited from GPT-2) |

## What it is (and isn't)

GPT-2 Large is a **base model**. It was never instruction-tuned or trained on chat data, so it predicts the next text and does not follow instructions. The template here frames each turn as a `Q:` / `A:` exchange, and stop sequences cut it off after one answer line, so it gives short answers.

**Good for:**
- Short factual questions and one-line answers
- Raw text continuation (story starts, article openings)
- Experiments, benchmarks, and learning how prompt templates steer a base model
- Running on low-end hardware (~20 tok/s on CPU)

**Not good for:**
- Code: its output is not valid, working code
- Multi-step instructions, math, or reasoning
- Factual accuracy: it writes smoothly and confidently makes things up
- Long answers: output stops at the first newline

## Example output

| Prompt | Response |
|---|---|
| Hi, who are you? | I'm a friendly person that helps people. |
| What is the capital of France? | Paris. |
| What is 2+2? | Two and two are equal to four. |
| What color is the sky? | It's blue! |

## Raw completion mode

To skip the Q&A template and use GPT-2 as a plain text continuer:

```sh
curl http://localhost:11434/api/generate -d '{
  "model": "treyleo16/gpt-2",
  "raw": true,
  "prompt": "Once upon a time in a small mountain town,",
  "options": { "stop": [], "num_predict": 200, "temperature": 0.8 }
}'
```

## Tips

- Keep `num_ctx` at **1024 or lower**. GPT-2 can't handle longer context.
- For longer answers, remove the `\n` stop and keep `"Q:"`. Output gets longer but wanders more.
- Lower temperature (0.3–0.5) gives more on-topic answers. Higher (0.8–1.0) works better for creative text.

## Credits

- **Base model:** [GPT-2](https://github.com/openai/gpt-2) by OpenAI, *"Language Models are Unsupervised Multitask Learners"* (Radford et al., 2019)
- **Packaging, template, and tuning:** [treyleo16](https://ollama.com/treyleo16)

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