The upstream model is an instruction-tuned post-train of talkie-lm/talkie-1930-13b-base, fine-tuned from instruction-response pairs extracted from pre-1931 reference works and then refined with online DPO, according to the original model card.

The upstream instruction-tuned checkpoint is already BF16. This repository adds Transformers AutoModelForCausalLM / AutoTokenizer support, a chat template matching the Talkie reference code, and BF16 sharded safetensors.

This is not an official Talkie release; refer to the upstream model card for the author-provided provenance and usage notes.

Source Model

Original model: talkie-lm/talkie-1930-13b-it
Talkie report: talkie-lm.com
Reference code: github.com/talkie-lm/talkie

Conversion Details

Weight dtype: BF16
Weight format: sharded safetensors
Context length: 4096 tokens
Architecture: custom Talkie code loaded with trust_remote_code=True
Tokenizer: Talkie tiktoken-compatible tokenizer exposed through AutoTokenizer

The public reference configuration originally advertised 2,048 positions, but the Talkie team later clarified that the model was trained with a 4,096-token context. This conversion uses the corrected 4,096-token limit.

Usage

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

path = "xlr8harder/talkie-1930-13b-it-tf"
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    path,
    trust_remote_code=True,
    dtype=torch.bfloat16,
    device_map={"": "cuda"},
    use_safetensors=True,
)

For chat-style prompts:

python

messages = [{"role": "user", "content": "Write an essay predicting life in 1960."}]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
).to("cuda")
output = model.generate(**inputs, max_new_tokens=128)
reply = output[0, inputs["input_ids"].shape[-1]:]
print(tokenizer.decode(reply, skip_special_tokens=True))

vLLM

The included remote-code model implements the Transformers attention-interface hooks expected by vLLM's Transformers modeling backend. For compatibility with that backend, the original single-scalar lm_head_gain is folded into lm_head.weight during conversion; the other Talkie gain parameters remain explicit model parameters. Using vLLM's logit_scale-style approach was not used because it applies scaling after the output matmul, while Talkie applies the gain to the head weight before the matmul. In BF16 this can introduce small rounding differences and, in smoke tests, changed one near-tied top-token ordering.

bash

vllm serve xlr8harder/talkie-1930-13b-it-tf \
  --task generate \
  --model-impl transformers \
  --trust-remote-code \
  --dtype bfloat16 \
  --max-model-len 4096

Validation

The Transformers safetensors model was compared against the original Talkie IT checkpoint on a forward-pass smoke test. The top-10 next-token ordering matched exactly; observed max absolute logit difference was 0.25.

davidafrica/talkie-1930-13b-it

talkie-1930-13b-it-tf (Transformers + safetensors conversion)

Source Model

Conversion Details

Usage

vLLM

Validation

davidafrica/
talkie-1930-13b-it