Instructions to use IvmeLabs/Ivme-Conversate-v2-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IvmeLabs/Ivme-Conversate-v2-Base with Transformers:

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

pipe = pipeline("text-generation", model="IvmeLabs/Ivme-Conversate-v2-Base", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("IvmeLabs/Ivme-Conversate-v2-Base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IvmeLabs/Ivme-Conversate-v2-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IvmeLabs/Ivme-Conversate-v2-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IvmeLabs/Ivme-Conversate-v2-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/IvmeLabs/Ivme-Conversate-v2-Base

SGLang

How to use IvmeLabs/Ivme-Conversate-v2-Base 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 "IvmeLabs/Ivme-Conversate-v2-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IvmeLabs/Ivme-Conversate-v2-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "IvmeLabs/Ivme-Conversate-v2-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IvmeLabs/Ivme-Conversate-v2-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use IvmeLabs/Ivme-Conversate-v2-Base with Docker Model Runner:
```
docker model run hf.co/IvmeLabs/Ivme-Conversate-v2-Base
```

ereniko commited on Jul 7

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eeec8c2

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@@ -54,15 +54,16 @@ Nearly every setting above matches v1 on purpose. The point of v2 was to isolate
 ## Benchmarks
-Benchmarks were run with a custom harness this time. We won't put İvme-Conversate-v2 to leaderboards for now until we verify our benchmark produces real results.
 | Benchmark | v1 | v2 |
 |---|---|---|
-| WikiText-2 (byte perplexity) ↓ | 2.96 | **2.50** |
-| BLiMP (macro-average) ↑ | 61.40% | **73.19%** |
-| ARC-Easy ↑ | 30.85% | **43.14%** |
-Every metric improved, and the ARC-Easy jump in particular is bigger than you would expect from data and training changes alone at this parameter count.
 ### BLiMP paradigm breakdown
@@ -70,22 +71,22 @@ Strong on core agreement paradigms:
 | Paradigm | Accuracy |
 |---|---|
-| existential_there_quantifiers_1 | 99.10% |
-| anaphor_number_agreement | 98.30% |
-| sentential_negation_npi_licensor_present | 98.10% |
-| determiner_noun_agreement_1 | 96.60% |
-| determiner_noun_agreement_2 | 95.40% |
-| anaphor_gender_agreement | 90.70% |
 Weaker on long-distance dependencies and island constraints, a known hard case for small models:
 | Paradigm | Accuracy |
 |---|---|
-| existential_there_quantifiers_2 | 18.90% |
-| left_branch_island_echo_question | 27.80% |
-| sentential_subject_island | 33.80% |
-| only_npi_scope | 42.60% |
-| complex_NP_island | 50.30% |
 This pattern, strong local agreement paired with weaker long-distance syntax, is typical for models at this scale.

 ## Benchmarks
+Benchmarks were run with lm-evaluation-harness via a custom model adapter (previous numbers from an internal script have been superseded by these).
 | Benchmark | v1 | v2 |
 |---|---|---|
+| WikiText-2 (byte perplexity) ↓ | 2.96 | **2.2250** |
+| BLiMP (macro-average, 68 paradigms) ↑ | 61.40% | **75.09%** |
+| ARC-Easy (acc_norm) ↑ | 30.85% | **39.98%** |
+| ARC-Easy (acc) ↑ | — | **43.56%** |
+Every metric improved. The ARC-Easy acc_norm gain (30.85% → 39.98%) is more modest than the raw acc numbers alone suggest, but still a real improvement over v1.
 ### BLiMP paradigm breakdown
 | Paradigm | Accuracy |
 |---|---|
+| blimp_principle_A_case_1 | 100.00% |
+| blimp_existential_there_quantifiers_1 | 99.40% |
+| blimp_anaphor_number_agreement | 98.00% |
+| blimp_sentential_negation_npi_licensor_present | 97.30% |
+| blimp_determiner_noun_agreement_1 | 96.90% |
+| blimp_principle_A_domain_1 | 95.40% |
 Weaker on long-distance dependencies and island constraints, a known hard case for small models:
 | Paradigm | Accuracy |
 |---|---|
+| blimp_wh_vs_that_with_gap_long_distance | 17.20% |
+| blimp_existential_there_quantifiers_2 | 30.20% |
+| blimp_left_branch_island_echo_question | 31.40% |
+| blimp_superlative_quantifiers_2 | 33.20% |
+| blimp_principle_A_reconstruction | 36.10% |
 This pattern, strong local agreement paired with weaker long-distance syntax, is typical for models at this scale.