---
language:
- en
base_model:
- talkie-lm/talkie-1930-13b-base
license: apache-2.0
pipeline_tag: text-generation
tags:
- talkie
- vllm
---

# talkie-1930-13b-base (vLLM-servable repackage)

This is a HuggingFace + vLLM-ready repackage of [`talkie-lm/talkie-1930-13b-base`](https://huggingface.co/talkie-lm/talkie-1930-13b-base). The original release ships as a raw torch checkpoint (`final.ckpt`) plus a tiktoken vocab (`vocab.txt`), with no `config.json`, `tokenizer.json`, or HF modeling code, so it can't be loaded by `transformers` or served by vLLM out of the box.

`talkie-1930-13b-base` is a 13B parameter base (completion) language model from the [talkie-lm](https://talkie-lm.com/) project, pretrained on ~260B tokens of pre-1931 English text. This is the **base** model — it is not instruction-tuned and has no chat template; prompt it with raw text and let it continue.

## What this repo adds

| File | What it is |
|---|---|
| `model.safetensors` | bf16 weights, ~26 GB. `lm_head_gain` (a learned scalar) is pre-multiplied into `lm_head.weight` so vLLM's transformers backend doesn't need to know about it. |
| `config.json` | `TalkieConfig` (vocab=65536, hidden=5120, 40 layers × 40 heads, head_dim=128, ctx=4096, RoPE θ=1e6) plus `auto_map` for `AutoConfig`/`AutoModel`/`AutoModelForCausalLM`. |
| `tokenizer.json`, `tokenizer_config.json` | HF fast BPE built from the original `vocab.txt` (ranks < 65535) with `<|endoftext|>` at id 65535. EOS = pad = `<\|endoftext\|>`. No chat specials (base model). |
| `generation_config.json` | `eos_token_id=65535`, `pad_token_id=65535`. |
| `modeling_talkie.py`, `configuration_talkie.py` | HF `PreTrainedModel` implementation with `ALL_ATTENTION_FUNCTIONS` dispatch (vLLM transformers-backend compatible). |

The architecture and tokenizer BPE ranks are identical to the IT repackage; the only differences are `vocab_size` (65536 vs 65540), the absence of the 4 chat special tokens, and no chat template.

## Serving with vLLM

```bash
vllm serve agaralon/talkie-1930-13b-base-vllm \
    --model-impl transformers \
    --trust-remote-code \
    --dtype bfloat16 \
    --max-model-len 4096
```

Then hit it as an OpenAI-style **completions** endpoint (base model — use `/v1/completions`, not chat):

```bash
curl http://localhost:8000/v1/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "agaralon/talkie-1930-13b-base-vllm",
        "prompt": "In the year 1925, the great city of London",
        "temperature": 0.8,
        "max_tokens": 120
    }'
```

### Sampling notes

- **Use `temperature ≥ 0.5`** — greedy decoding (`temperature=0`) can collapse into single-token loops on this architecture.
- bf16 only — fp8 is broken on this architecture.

## Plain HuggingFace usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tok = AutoTokenizer.from_pretrained("agaralon/talkie-1930-13b-base-vllm", trust_remote_code=True)
m = AutoModelForCausalLM.from_pretrained(
    "agaralon/talkie-1930-13b-base-vllm", trust_remote_code=True, dtype=torch.bfloat16,
).cuda().eval()

ids = tok(["In the year 1925, the great city of London"], return_tensors="pt").to("cuda")
out = m.generate(**ids, max_new_tokens=120, do_sample=True, temperature=0.8, top_p=0.9,
                 pad_token_id=tok.pad_token_id, eos_token_id=tok.eos_token_id)
print(tok.decode(out[0, ids.input_ids.shape[1]:], skip_special_tokens=True))
```

## Provenance

- Weights: `final.ckpt` from `talkie-lm/talkie-1930-13b-base`, with `lm_head_gain.w_g` baked into `lm_head.weight` and cast to bf16.
- Tokenizer: built from `vocab.txt` from the same release (ranks < 65535, then `<|endoftext|>` at id 65535).
- Modeling code: adapted from the IT repackage [`awilliamson/talkie-1930-13b-it-vllm`](https://huggingface.co/awilliamson/talkie-1930-13b-it-vllm).

## License

Apache 2.0, matching the upstream `talkie-lm/talkie-1930-13b-base` release.